Integrating LangChain with Flask for Code Debugging

Install CKEditor for React

npm install @ckeditor/ckeditor5-react @ckeditor/ckeditor5-build-classic

import React, { useState } from 'react';
import CKEditor from '@ckeditor/ckeditor5-react';
import ClassicEditor from '@ckeditor/ckeditor5-build-classic';

function App() {
  const [code, setCode] = useState('');
  const [error, setError] = useState('');
  const [correctedCode, setCorrectedCode] = useState('');

  // Handle form submission
  const handleSubmit = async (event) => {
    event.preventDefault();

    const requestBody = {
      code_with_error: code,
      error_message: error,
    };

    try {
      const response = await fetch('http://localhost:5000/debug_and_generate_code', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify(requestBody),
      });

      const result = await response.json();
      setCorrectedCode(result.corrected_code);
    } catch (error) {
      console.error("Error: ", error);
    }
  };

  return (
    <div className="App">
      <h1>Code Debugger</h1>

      <form onSubmit={handleSubmit}>
        <div>
          <h3>Enter Code with Error</h3>
          <CKEditor
            editor={ClassicEditor}
            data={code}
            onChange={(event, editor) => setCode(editor.getData())}
          />
        </div>

        <div>
          <h3>Enter Error Message</h3>
          <CKEditor
            editor={ClassicEditor}
            data={error}
            onChange={(event, editor) => setError(editor.getData())}
          />
        </div>

        <button type="submit">Submit</button>
      </form>

      {correctedCode && (
        <div>
          <h3>Corrected Code</h3>
          <pre>{correctedCode}</pre>
        </div>
      )}
    </div>
  );
}

export default App;

from flask import Flask, request, jsonify
import openai
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.agents import RunnableParallel

app = Flask(__name__)

# Set OpenAI API key
openai.api_key = "YOUR_OPENAI_API_KEY"

# Initialize LangChain LLM (OpenAI GPT-3 in this case)
llm = OpenAI(model="text-davinci-003", temperature=0.7)

@app.route("/debug_and_generate_code", methods=["POST"])
def debug_and_generate_code():
    try:
        # Extract the data from the incoming request
        data = request.get_json()
        code_with_error = data.get("code_with_error")
        error_message = data.get("error_message")

        if not code_with_error or not error_message:
            return jsonify({"error": "Both code and error message are required."}), 400

        # Step 1: Create a prompt template for code debugging and generation
        code_debug_prompt = PromptTemplate(
            input_variables=["code", "error"],
            template="""Here is a Python function with an error:

            {code}

            The error message is:

            {error}

            Please debug the function and regenerate the corrected code."""
        )

        # Step 2: Initialize LLMChain with the prompt and LLM
        code_debug_chain = LLMChain(prompt=code_debug_prompt, llm=llm)

        # Step 3: Use LangChain to invoke the chain and get the corrected code
        corrected_code = code_debug_chain.run({
            "code": code_with_error,
            "error": error_message
        })

        # Step 4: Return the corrected code
        return jsonify({"corrected_code": corrected_code})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

import React, { useState } from 'react';

const ValidationCodeGenerator = () => {
  const [language, setLanguage] = useState('');
  const [validationType, setValidationType] = useState('');
  const [generatedCode, setGeneratedCode] = useState('');

  const handleSubmit = async () => {
    const requestBody = {
      language: language,
      validationType: validationType,
    };

    const response = await fetch('http://localhost:5000/generate_validation_code', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(requestBody),
    });

    const data = await response.json();
    setGeneratedCode(data.generated_code);  // Display generated code
  };

  return (
    <div>
      <select onChange={(e) => setLanguage(e.target.value)} value={language}>
        <option value="">Select Language</option>
        <option value="react">React</option>
        <option value="laravel">Laravel</option>
        <option value="python">Python</option>
        <option value="flask">Flask</option>
        <option value="django">Django</option>
        <option value="php">PHP</option>
        <option value="javascript">JavaScript</option>
        <option value="jquery">jQuery</option>
      </select>

      <select onChange={(e) => setValidationType(e.target.value)} value={validationType}>
        <option value="">Select Validation Type</option>
        <option value="phone">Phone</option>
        <option value="email">Email</option>
        <option value="digits">Digits</option>
        <option value="alphanumeric">Alphanumeric</option>
        <option value="digits_length">Digits Length</option>
      </select>

      <button onClick={handleSubmit}>Generate Validation Code</button>

      {generatedCode && (
        <div>
          <h3>Generated Code:</h3>
          <pre>{generatedCode}</pre>
        </div>
      )}
    </div>
  );
};


export default ValidationCodeGenerator;

from flask import Flask, request, jsonify
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Initialize Flask app
app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(model="text-davinci-003", temperature=0.7)

# Define the prompt template for generating validation code dynamically
validation_code_prompt = PromptTemplate(
    input_variables=["language", "validation_type"],
    template="""
    Based on the programming language {language} and the validation type {validation_type}, generate the code to validate an input field. 
    The validation type can be:
    - 'phone': Validate a phone number
    - 'email': Validate an email address
    - 'digits': Validate that the input is a digit
    - 'alphanumeric': Validate that the input is alphanumeric
    - 'digits_length': Validate that the input is a specific length of digits

    Example:
    - For React, use JavaScript and Regex to validate.
    - For Laravel (PHP), use PHP validation methods.
    - For Python, use the re module for validation.
    - For Flask, use Python's re module or custom validation functions.
    - For Django, use Django's built-in validators.
    - For JavaScript, use JavaScript's Regex.
    - For jQuery, use jQuery validation methods.

    Please generate the proper validation code for the selected language and validation type.
    """
)

# Create LangChain with the prompt template for validation code generation
validation_code_chain = LLMChain(prompt=validation_code_prompt, llm=llm)

@app.route("/generate_validation_code", methods=["POST"])
def generate_validation_code():
    try:
        # Get incoming data from the frontend
        data = request.get_json()
        language = data.get("language")
        validation_type = data.get("validationType")

        # Generate the corresponding validation code
        validation_code = validation_code_chain.run({
            "language": language,
            "validation_type": validation_type
        })

        # Return the generated validation code as response
        return jsonify({"generated_code": validation_code})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(debug=True)

React: Use JavaScript regular expressions (/^\d{10}$/)
Laravel: Use Laravel's built-in validation ('phone' => 'required|digits:10')
Python: Use re.match(r'^\d{10}$', phone_number)
Django: Use Django’s RegexValidator

React: Use a regular expression for email validation.
Laravel: Use email validation rule.
Python: Use re.match(r"[^@]+@[^@]+\.[^@]+", email)
Django: Use Django’s EmailValidator

{
  "language": "python",
  "validationType": "phone"
}

{
  "generated_code": "import re\nphone_number = '1234567890'\nif re.match(r'^\d{10}$', phone_number):\n    print('Valid phone number')\nelse:\n    print('Invalid phone number')"
}

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Detect Programming Language
language_detection_prompt = PromptTemplate(
    input_variables=["code"],
    template="Identify the programming language of the following code: {code}"
)

# Detect Data Structure Format
format_detection_prompt = PromptTemplate(
    input_variables=["code"],
    template="Identify the format of the data in the following code. Possible formats: array, associative array, dictionary, nested array: {code}"
)

# Generate JSON conversion code based on detected format and language
json_generation_prompt = PromptTemplate(
    input_variables=["language", "format", "code"],
    template="""
    Based on the programming language {language} and the data format {format}, 
    generate the corresponding JSON conversion code for the data:
    Code: {code}
    """
)

# Create LLMChain for Language Detection
language_detection_chain = LLMChain(prompt=language_detection_prompt, llm=llm)

# Create LLMChain for Format Detection
format_detection_chain = LLMChain(prompt=format_detection_prompt, llm=llm)

# Create LLMChain for JSON Code Generation
json_generation_chain = LLMChain(prompt=json_generation_prompt, llm=llm)

from flask import Flask, request, jsonify
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Initialize the Flask app
app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(model="text-davinci-003", temperature=0.7)

@app.route("/generate_json", methods=["POST"])
def generate_json():
    try:
        # Get the input code from the request
        data = request.get_json()
        code = data.get("code")

        # Step 1: Detect the Programming Language
        language_result = language_detection_chain.run({"code": code})

        # Step 2: Detect the Data Structure Format
        format_result = format_detection_chain.run({"code": code})

        # Step 3: Generate the corresponding JSON conversion code
        json_code = json_generation_chain.run({
            "language": language_result,
            "format": format_result,
            "code": code
        })

        # Return the generated JSON code as a response
        return jsonify({"language": language_result, "format": format_result, "json_code": json_code})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(debug=True)

Copy
{
  "code": "$processed_lines_json = json_encode($processed_lines);"
}

{
  "language": "PHP",
  "format": "associative array",
  "json_code": "let processed_lines_json = JSON.stringify(processed_lines);"
}

import React, { useState } from "react";

const JsonConverter = () => {
  const [selectedLanguage, setSelectedLanguage] = useState("");
  const [selectedFormat, setSelectedFormat] = useState("");
  const [code, setCode] = useState("");
  const [jsonCode, setJsonCode] = useState("");

  const handleSubmit = async () => {
    const response = await fetch("http://localhost:5000/generate_json", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ code }),
    });

    const data = await response.json();
    setJsonCode(data.json_code);
  };

  return (
    <div>
      <textarea
        placeholder="Paste code here"
        value={code}
        onChange={(e) => setCode(e.target.value)}
        style={{ width: "100%", height: "100px", marginBottom: "10px" }}
      ></textarea>
      <div>
        <h4>Select Language:</h4>
        <input
          type="radio"
          id="php"
          name="language"
          value="php"
          onChange={(e) => setSelectedLanguage(e.target.value)}
        />
        <label htmlFor="php">PHP</label>
        <input
          type="radio"
          id="python"
          name="language"
          value="python"
          onChange={(e) => setSelectedLanguage(e.target.value)}
        />
        <label htmlFor="python">Python</label>
        {/* Add more language options here */}
      </div>
      <div>
        <h4>Select Data Format:</h4>
        <input
          type="radio"
          id="associative_array"
          name="format"
          value="associative_array"
          onChange={(e) => setSelectedFormat(e.target.value)}
        />
        <label htmlFor="associative_array">Associative Array</label>
        <input
          type="radio"
          id="array"
          name="format"
          value="array"
          onChange={(e) => setSelectedFormat(e.target.value)}
        />
        <label htmlFor="array">Array</label>
        {/* Add more format options here */}
      </div>
      <button onClick={handleSubmit}>Generate JSON Code</button>
      <div>
        <h3>Generated JSON Code:</h3>
        <pre>{jsonCode}</pre>
      </div>
    </div>
  );
};

export default JsonConverter;

line_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify the specific line that causes the error based on the given error message:
    Code: {code}
    Error: {error_message}
    Please indicate which line is problematic and why."""
)

variable_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify which variable is causing the error:
    Code: {code}
    Error: {error_message}
    Identify any variables that are causing issues (e.g., undefined, null, incorrect type)."""
)

stack_trace_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message", "stack_trace"],
    template="""Using the stack trace provided, follow the path from the point of failure to its origin in the code. Identify the functions or methods involved:
    Code: {code}
    Error: {error_message}
    Stack Trace: {stack_trace}
    Determine which function or line in the code triggered the error and why."""
)

data_flow_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the flow of data in the following code and trace where the error happens based on the provided error message:
    Code: {code}
    Error: {error_message}
    Identify the data flow and which part of the code breaks or causes the error."""
)

dependency_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify which external dependency or call might be causing the error:
    Code: {code}
    Error: {error_message}
    Look for any external libraries, network calls, database queries, or I/O operations that may have failed."""
)

control_flow_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the flow of control structures (loops, conditionals, try-except blocks) in the code and determine if the error is caused by an incorrect branching logic:
    Code: {code}
    Error: {error_message}
    Identify any logical issues with control flow or conditions that may lead to errors."""
)

function_call_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following function calls in the code and identify which function call is causing the error:
    Code: {code}
    Error: {error_message}
    Look at the arguments, return values, and function signatures to pinpoint the issue."""
)

unreachable_code_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify if there is any unreachable code causing the error:
    Code: {code}
    Error: {error_message}
    Detect any dead code or improperly ordered blocks that prevent execution."""
)

exception_handling_review_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Review the exception handling in the following code and identify any missed edge cases or improperly handled exceptions:
    Code: {code}
    Error: {error_message}
    Check if the error handling strategy is correct and if edge cases are properly handled."""
)

from flask import Flask, request, jsonify
import langchain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

app = Flask(__name__)

# Initialize OpenAI LLM
llm = OpenAI(api_key="your-openai-api-key")

# Define prompt templates for different types of error analysis
line_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify the specific line that causes the error based on the given error message:
    Code: {code}
    Error: {error_message}
    Please indicate which line is problematic and why."""
)

variable_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and identify which variable is causing the error:
    Code: {code}
    Error: {error_message}
    Identify any variables that are causing issues (e.g., undefined, null, incorrect type)."""
)

stack_trace_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_message", "stack_trace"],
    template="""Using the stack trace provided, follow the path from the point of failure to its origin in the code. Identify the functions or methods involved:
    Code: {code}
    Error: {error_message}
    Stack Trace: {stack_trace}
    Determine which function or line in the code triggered the error and why."""
)

# More prompts for data flow, control flow, dependency analysis, etc. can be added similarly...

# Setup LangChain with all the prompts
@app.route("/analyze", methods=["POST"])
def analyze_code():
    data = request.json
    code = data.get("code")
    error_message = data.get("error_message")
    stack_trace = data.get("stack_trace", "")
    analysis_type = data.get("analysis_type")  # Identify the type of analysis

    # Select appropriate prompt based on analysis type
    if analysis_type == "line_analysis":
        prompt = line_analysis_prompt
    elif analysis_type == "variable_analysis":
        prompt = variable_analysis_prompt
    elif analysis_type == "stack_trace_analysis":
        prompt = stack_trace_analysis_prompt
    # Add additional checks for other analysis types...

    # Create LangChain chain to process the selected analysis
    chain = LLMChain(llm=llm, prompt=prompt)
    analysis_result = chain.run(code=code, error_message=error_message, stack_trace=stack_trace)

    return jsonify({"analysis": analysis_result})

if __name__ == "__main__":
    app.run(debug=True)

import React, { useState } from "react";

const App = () => {
  const [language, setLanguage] = useState("");
  const [analysisType, setAnalysisType] = useState("line_analysis");
  const [code, setCode] = useState("");
  const [errorMessage, setErrorMessage] = useState("");
  const [stackTrace, setStackTrace] = useState("");
  const [result, setResult] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("/analyze", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        code,
        error_message: errorMessage,
        stack_trace: stackTrace,
        analysis_type: analysisType,
      }),
    });

    const data = await response.json();
    setResult(data.analysis);
  };

  return (
    <div>
      <h1>Error Analysis Tool</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Programming Language:</label>
          <select value={language} onChange={(e) => setLanguage(e.target.value)}>
            <option value="">Select Language</option>
            <option value="python">Python</option>
            <option value="javascript">JavaScript</option>
            <option value="java">Java</option>
            {/* Add more languages as needed */}
          </select>
        </div>

        <div>
          <label>Analysis Type:</label>
          <select
            value={analysisType}
            onChange={(e) => setAnalysisType(e.target.value)}
          >
            <option value="line_analysis">Line-by-Line Analysis</option>
            <option value="variable_analysis">Variable Analysis</option>
            <option value="stack_trace_analysis">Stack Trace Analysis</option>
            {/* Add other analysis types here */}
          </select>
        </div>

        <div>
          <label>Code:</label>
          <textarea
            rows="10"
            cols="50"
            value={code}
            onChange={(e) => setCode(e.target.value)}
          ></textarea>
        </div>

        <div>
          <label>Error Message:</label>
          <textarea
            rows="4"
            cols="50"
            value={errorMessage}
            onChange={(e) => setErrorMessage(e.target.value)}
          ></textarea>
        </div>

        <div>
          <label>Stack Trace (Optional):</label>
          <textarea
            rows="4"
            cols="50"
            value={stackTrace}
            onChange={(e) => setStackTrace(e.target.value)}
          ></textarea>
        </div>

        <button type="submit">Analyze</button>
      </form>

      <h2>Analysis Result</h2>
      <pre>{result}</pre>
    </div>
  );
};

export default App;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(api_key="your-openai-api-key")

# Define error classification prompt
error_classification_prompt = PromptTemplate(
    input_variables=["error_message"],
    template="""Classify the following error message into one of the following types: 
    SyntaxError, RuntimeError, TypeError, AttributeError, NameError, IndexError, ValueError, ImportError, 
    IndentationError, FileError, MemoryError, RecursionError. 
    Error message: {error_message}"""
)

# Initialize LLMChain for error classification
error_classification_chain = LLMChain(llm=llm, prompt=error_classification_prompt)

# Define debugging prompts for various error types
syntax_error_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    Please identify the syntax issue and suggest how to fix it."""
)

runtime_error_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    Please identify the runtime issue (e.g., exception or unexpected behavior) and suggest how to resolve it."""
)

# Other error prompts (TypeError, AttributeError, etc.) are defined similarly...

# Main route for error classification and debugging
@app.route("/analyze", methods=["POST"])
def analyze_code():
    data = request.json
    code = data.get("code")
    error_message = data.get("error_message")

    # Classify error type using LangChain
    error_type = error_classification_chain.run({"error_message": error_message})

    # Based on error type, run the appropriate debugging chain
    if error_type == "SyntaxError":
        prompt = syntax_error_prompt
    elif error_type == "RuntimeError":
        prompt = runtime_error_prompt
    # More elif conditions for other error types like TypeError, NameError, etc.
    else:
        prompt = None  # If no match found, you could set this to a generic error analysis chain

    if prompt:
        chain = LLMChain(llm=llm, prompt=prompt)
        analysis_result = chain.run(code=code, error_message=error_message)
    else:
        analysis_result = "Error type not recognized or supported for debugging."

    return jsonify({"analysis": analysis_result})

if __name__ == "__main__":
    app.run(debug=True)

import React, { useState } from "react";

const App = () => {
  const [errorMessage, setErrorMessage] = useState("");
  const [code, setCode] = useState("");
  const [errorType, setErrorType] = useState("SyntaxError");
  const [result, setResult] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("/analyze", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        code,
        error_message: errorMessage,
      }),
    });

    const data = await response.json();
    setResult(data.analysis);
  };

  return (
    <div>
      <h1>Error Classification and Debugging Tool</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Error Message:</label>
          <input
            type="text"
            value={errorMessage}
            onChange={(e) => setErrorMessage(e.target.value)}
            placeholder="Enter error message"
          />
        </div>

        <div>
          <label>Code:</label>
          <textarea
            rows="10"
            cols="50"
            value={code}
            onChange={(e) => setCode(e.target.value)}
            placeholder="Paste code here"
          ></textarea>
        </div>

        <div>
          <label>Error Type:</label>
          <select value={errorType} onChange={(e) => setErrorType(e.target.value)}>
            <option value="SyntaxError">SyntaxError</option>
            <option value="RuntimeError">RuntimeError</option>
            <option value="TypeError">TypeError</option>
            <option value="AttributeError">AttributeError</option>
            <option value="NameError">NameError</option>
            <option value="IndexError">IndexError</option>
            <option value="ValueError">ValueError</option>
            <option value="ImportError">ImportError</option>
            <option value="IndentationError">IndentationError</option>
            <option value="FileError">FileError</option>
            <option value="MemoryError">MemoryError</option>
            <option value="RecursionError">RecursionError</option>
          </select>
        </div>

        <button type="submit">Analyze</button>
      </form>

      <h2>Analysis Result</h2>
      <pre>{result}</pre>
    </div>
  );
};

export default App;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(api_key="your-openai-api-key")

# Define prompts for various error types

# 1. **SyntaxError Fix**
syntax_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to a syntax issue, suggest how to fix the syntax problem."""
)

# 2. **RuntimeError Fix**
runtime_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is caused by an issue during runtime, provide the reason and suggest a fix."""
)

# 3. **TypeError Fix**
type_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to a type mismatch, suggest how to cast or refactor the code to resolve the issue."""
)

# 4. **AttributeError Fix**
attribute_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to an invalid attribute or method, suggest the correct attribute or method to access."""
)

# 5. **NameError Fix**
name_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to an undefined or misspelled variable or function, suggest the correct definition."""
)

# 6. **IndexError Fix**
index_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to an index being out of range, suggest how to ensure the index is valid."""
)

# 7. **ValueError Fix**
value_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is caused by an invalid value, suggest how to validate or correct the input."""
)

# 8. **ImportError Fix**
import_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to an incorrect import or module not found, suggest how to correctly import the required module."""
)

# 9. **IndentationError Fix**
indentation_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to improper indentation, suggest how to correct the indentation."""
)

# 10. **FileError Fix**
file_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is related to file operations (e.g., file not found, permission denied), suggest how to resolve the file handling issue."""
)

# 11. **MemoryError Fix**
memory_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to memory-related issues, suggest how to optimize memory usage."""
)

# 12. **RecursionError Fix**
recursion_error_fix_prompt = PromptTemplate(
    input_variables=["code", "error_message"],
    template="""Analyze the following code and error message:
    Code: {code}
    Error: {error_message}
    If the error is due to an infinite recursion or exceeding recursion depth, suggest how to modify the recursion base case or logic."""
)

# Main route for self-healing and contextual error resolution
@app.route("/self-heal", methods=["POST"])
def self_heal_code():
    data = request.json
    code = data.get("code")
    error_message = data.get("error_message")

    # Classify error type using a simple conditional check (could be replaced with LangChain for classification)
    if "SyntaxError" in error_message:
        prompt = syntax_error_fix_prompt
    elif "RuntimeError" in error_message:
        prompt = runtime_error_fix_prompt
    elif "TypeError" in error_message:
        prompt = type_error_fix_prompt
    elif "AttributeError" in error_message:
        prompt = attribute_error_fix_prompt
    elif "NameError" in error_message:
        prompt = name_error_fix_prompt
    elif "IndexError" in error_message:
        prompt = index_error_fix_prompt
    elif "ValueError" in error_message:
        prompt = value_error_fix_prompt
    elif "ImportError" in error_message:
        prompt = import_error_fix_prompt
    elif "IndentationError" in error_message:
        prompt = indentation_error_fix_prompt
    elif "FileError" in error_message:
        prompt = file_error_fix_prompt
    elif "MemoryError" in error_message:
        prompt = memory_error_fix_prompt
    elif "RecursionError" in error_message:
        prompt = recursion_error_fix_prompt
    else:
        prompt = None  # If no match, generic error handling can be applied

    # Run LangChain to suggest fixes based on the error type
    if prompt:
        chain = LLMChain(llm=llm, prompt=prompt)
        fix_suggestion = chain.run(code=code, error_message=error_message)
    else:
        fix_suggestion = "Error type not recognized or no fix available."

    return jsonify({"fix_suggestion": fix_suggestion})

if __name__ == "__main__":
    app.run(debug=True)

import React, { useState } from "react";

const App = () => {
  const [code, setCode] = useState("");
  const [errorMessage, setErrorMessage] = useState("");
  const [result, setResult] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("/self-heal", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        code,
        error_message: errorMessage,
      }),
    });

    const data = await response.json();
    setResult(data.fix_suggestion);
  };

  return (
    <div>
      <h1>Self-Healing Code Tool</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Error Message:</label>
          <input
            type="text"
            value={errorMessage}
            onChange={(e) => setErrorMessage(e.target.value)}
            placeholder="Enter error message"
          />
        </div>

        <div>
          <label>Code:</label>
          <textarea
            rows="10"
            cols="50"
            value={code}
            onChange={(e) => setCode(e.target.value)}
            placeholder="Paste code here"
          ></textarea>
        </div>

        <button type="submit">Suggest Fix</button>
      </form>

      <h2>Fix Suggestion</h2>
      <pre>{result}</pre>
    </div>
  );
};

export default App;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(api_key="your-openai-api-key")

# Chain 1: Extract error details (line number, variable names, method calls)
extract_error_details_prompt = PromptTemplate(
    input_variables=["error_message", "code"],
    template="""Analyze the following error message and code:
    Code: {code}
    Error: {error_message}
    Extract the following details:
    - Line number where the error occurred.
    - Variables involved in the error (undefined, incorrect values, etc.).
    - Method calls that triggered the error.
    Provide these details in a structured format."""
)

# Chain 2: Trace the root cause and generate debugging suggestions
root_cause_analysis_prompt = PromptTemplate(
    input_variables=["code", "error_details"],
    template="""Based on the error details and the code, trace back to find the root cause of the error:
    Code: {code}
    Error Details: {error_details}
    Identify the specific root cause of the error (e.g., undefined variable, incorrect method call, out-of-bounds access).
    Suggest debugging steps to fix the issue."""
)

# Main route for contextualizing errors and identifying root causes

@app.route("/analyze-error", methods=["POST"])
def analyze_error():
    data = request.json
    code = data.get("code")
    error_message = data.get("error_message")

    # Step 1: Extract error details using Chain 1
    extract_chain = LLMChain(llm=llm, prompt=extract_error_details_prompt)
    error_details = extract_chain.run(error_message=error_message, code=code)

    # Step 2: Analyze the root cause using Chain 2
    root_cause_chain = LLMChain(llm=llm, prompt=root_cause_analysis_prompt)
    root_cause_analysis = root_cause_chain.run(code=code, error_details=error_details)

    # Return the results
    return jsonify({
        "error_details": error_details,
        "root_cause_analysis": root_cause_analysis
    })

if __name__ == "__main__":
    app.run(debug=True)

import React, { useState } from "react";

const App = () => {
  const [code, setCode] = useState("");
  const [errorMessage, setErrorMessage] = useState("");
  const [errorDetails, setErrorDetails] = useState("");
  const [rootCauseAnalysis, setRootCauseAnalysis] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("/analyze-error", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        code,
        error_message: errorMessage,
      }),
    });

    const data = await response.json();
    setErrorDetails(data.error_details);
    setRootCauseAnalysis(data.root_cause_analysis);
  };

  return (
    <div>
      <h1>Error Contextualization and Root Cause Analysis</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Error Message:</label>
          <input
            type="text"
            value={errorMessage}
            onChange={(e) => setErrorMessage(e.target.value)}
            placeholder="Enter error message"
          />
        </div>

        <div>
          <label>Code:</label>
          <textarea
            rows="10"
            cols="50"
            value={code}
            onChange={(e) => setCode(e.target.value)}
            placeholder="Paste code here"
          ></textarea>
        </div>

        <button type="submit">Analyze</button>
      </form>

      <h2>Error Details</h2>
      <pre>{errorDetails}</pre>

      <h2>Root Cause Analysis</h2>
      <pre>{rootCauseAnalysis}</pre>
    </div>
  );
};

export default App;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

app = Flask(__name__)

# Initialize LangChain with OpenAI LLM
llm = OpenAI(api_key="your-openai-api-key")

# Define the prompt for error prediction and preventive debugging
error_prediction_prompt = PromptTemplate(
    input_variables=["code"],
    template="""Analyze the following code and predict any potential errors that may occur during execution. Identify areas where the code is likely to fail and provide suggestions for preventive debugging:
    Code: {code}
    Provide suggestions to fix potential issues such as incorrect data types, out-of-bounds access, logical errors, or unhandled exceptions."""
)

# Main route for error prediction and preventive debugging

@app.route("/predict-errors", methods=["POST"])
def predict_errors():
    data = request.json
    code = data.get("code")

    # Run LangChain to predict potential errors and provide preventive debugging
    chain = LLMChain(llm=llm, prompt=error_prediction_prompt)
    prediction_result = chain.run(code=code)

    return jsonify({"prediction_result": prediction_result})

if __name__ == "__main__":
    app.run(debug=True)

import React, { useState } from "react";

const App = () => {
  const [code, setCode] = useState("");
  const [predictionResult, setPredictionResult] = useState("");

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("/predict-errors", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        code,
      }),
    });

    const data = await response.json();
    setPredictionResult(data.prediction_result);
  };

  return (
    <div>
      <h1>Error Prediction and Preventive Debugging Tool</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Code:</label>
          <textarea
            rows="10"
            cols="50"
            value={code}
            onChange={(e) => setCode(e.target.value)}
            placeholder="Paste your code here"
          ></textarea>
        </div>

        <button type="submit">Predict Errors</button>
      </form>

      <h2>Prediction Result</h2>
      <pre>{predictionResult}</pre>
    </div>
  );
};

export default App;

import React, { useState } from "react";

const App = () => {
  const [language, setLanguage] = useState("python");  // Default programming language
  const [errorMessage, setErrorMessage] = useState("");
  const [codeWithError, setCodeWithError] = useState("");
  const [result, setResult] = useState(null);

  const handleSubmit = async (e) => {
    e.preventDefault();

    const response = await fetch("http://localhost:5000/debug_and_generate_code", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        language,
        error_message: errorMessage,
        code_with_error: codeWithError,
      }),
    });

    const data = await response.json();
    setResult(data.results);
  };

  return (
    <div>
      <h1>Error Debugging and Code Generation Tool</h1>
      <form onSubmit={handleSubmit}>
        <div>
          <label>Programming Language:</label>
          <select value={language} onChange={(e) => setLanguage(e.target.value)}>
            <option value="python">Python</option>
            <option value="javascript">JavaScript</option>
            <option value="java">Java</option>
            <option value="ruby">Ruby</option>
            <option value="c++">C++</option>
            {/* Add more languages as necessary */}
          </select>
        </div>

        <div>
          <label>Error Message:</label>
          <textarea
            rows="4"
            cols="50"
            value={errorMessage}
            onChange={(e) => setErrorMessage(e.target.value)}
            placeholder="Paste the error message here"
          />
        </div>

        <div>
          <label>Code with Error:</label>
          <textarea
            rows="10"
            cols="50"
            value={codeWithError}
            onChange={(e) => setCodeWithError(e.target.value)}
            placeholder="Paste the code with the error here"
          />
        </div>

        <button type="submit">Debug and Regenerate Code</button>
      </form>

      {result && (
        <div>
          <h2>Results</h2>
          <h3>Error Type:</h3>
          <pre>{result.error_type}</pre>

          <h3>Fix Suggestion:</h3>
          <pre>{result.fix_suggestion}</pre>

          <h3>Error Trace (Root Cause):</h3>
          <pre>{result.error_trace}</pre>

          <h3>Corrected Code:</h3>
          <pre>{result.corrected_code}</pre>
        </div>
      )}
    </div>
  );
};

export default App;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import LLMChain, RunnableParallel
from langchain.agents import Runnable

# Initialize the Flask app
app = Flask(__name__)

# Set OpenAI API Key (replace with your API key)
openai.api_key = "YOUR_OPENAI_API_KEY"
llm = OpenAI(model="text-davinci-003", temperature=0.7)

@app.route("/debug_and_generate_code", methods=["POST"])
def debug_and_generate_code():
    try:
        # Get the code with error and error message from the request
        data = request.get_json()
        code_with_error = data.get("code_with_error")
        error_message = data.get("error_message")

        if not code_with_error or not error_message:
            return jsonify({"error": "Both code and error message are required."}), 400

        # Step 1: Identify the error type (SyntaxError, TypeError, etc.)
        error_type_prompt = PromptTemplate(
            input_variables=["error_message"],
            template="Classify the following error message: {error_message}. Possible types: SyntaxError, TypeError, AttributeError, IndexError, NameError, KeyError, etc."
        )

        # Step 2: Resolve the error based on its type (suggest a fix)
        error_resolution_prompt = PromptTemplate(
            input_variables=["code", "error_type"],
            template="The following code has a {error_type} error:\n{code}\nPlease suggest a fix for this error."
        )

        # Step 3: Trace the root cause of the error (which line/variable)
        error_trace_prompt = PromptTemplate(
            input_variables=["code", "error_type"],
            template="Analyze the following code for {error_type} error and determine the line or variable causing the issue.\n{code}"
        )

        # Step 4: Regenerate the code after applying the fix
        regenerate_code_prompt = PromptTemplate(
            input_variables=["code", "fix_suggestion"],
            template="Here is the code after fixing the error: {code}\nPlease regenerate the corrected version of the function or code."
        )

        # Create LLMChains for each task
        error_type_chain = LLMChain(prompt=error_type_prompt, llm=llm)
        error_resolution_chain = LLMChain(prompt=error_resolution_prompt, llm=llm)
        error_trace_chain = LLMChain(prompt=error_trace_prompt, llm=llm)
        regenerate_code_chain = LLMChain(prompt=regenerate_code_prompt, llm=llm)

        # Step 5: Run all chains in parallel using RunnableParallel
        parallel_chain = RunnableParallel(
            chains={
                "error_type_chain": error_type_chain,
                "error_resolution_chain": error_resolution_chain,
                "error_trace_chain": error_trace_chain,
                "regenerate_code_chain": regenerate_code_chain,
            }
        )

        # Execute the parallel chains
        print("Executing parallel chains for error debugging...")
        results = parallel_chain.invoke({
            "code": code_with_error,
            "error_message": error_message
        })

        # Process and combine the results
        result = {
            "error_type": results["error_type_chain"],
            "fix_suggestion": results["error_resolution_chain"],
            "error_trace": results["error_trace_chain"],
            "corrected_code": results["regenerate_code_chain"]
        }

        # Return the results
        return jsonify({"results": result})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

from langchain.prompts import PromptTemplate

# Prompt to analyze the dataset
dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and list the important features: missing values, outliers, data types, and suggest any necessary preprocessing steps. Dataset: {dataset}"
)

preprocessing_prompt = PromptTemplate(
    input_variables=["dataset_analysis"],
    template="Based on the dataset analysis: {dataset_analysis}, suggest appropriate preprocessing steps for this dataset."
)

hyperparameter_tuning_prompt = PromptTemplate(
    input_variables=["dataset_type", "task_type"],
    template="For a {dataset_type} dataset and a {task_type} task, suggest the best hyperparameters to tune and their possible ranges for optimal accuracy."
)

missing_value_imputation_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and identify columns with missing values. Suggest appropriate imputation methods for each column based on the data type: {dataset}"
)

outlier_detection_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and identify any potential outliers. Suggest methods to handle outliers in the data: {dataset}"
)

categorical_encoding_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="For the dataset: {dataset}, suggest appropriate encoding techniques for categorical features (e.g., one-hot encoding, label encoding)."
)

scaling_normalization_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="For the following dataset: {dataset}, recommend suitable scaling or normalization methods (e.g., Min-Max scaling, Standardization) for numerical features."
)

feature_selection_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Given the dataset: {dataset} and the task: {task_type}, recommend feature selection techniques that can help reduce overfitting and improve model performance."
)

model_selection_prompt = PromptTemplate(
    input_variables=["dataset_type", "task_type"],
    template="Based on the dataset type: {dataset_type} and task: {task_type}, suggest the best machine learning models to consider for this problem."
)

rf_hyperparameter_tuning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For a Random Forest model applied to the dataset: {dataset} and task: {task_type}, recommend which hyperparameters should be tuned and their possible value ranges."
)

cross_validation_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For the given dataset: {dataset} and task: {task_type}, suggest the most appropriate cross-validation strategy to evaluate the model's performance."
)

class_weighting_sampling_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Given the dataset: {dataset} and the task type: {task_type}, suggest methods to address class imbalance (e.g., class weighting, oversampling, undersampling)."
)

import openai
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SequentialChain
from langchain.llms import OpenAI
from flask import Flask, request, jsonify

# Initialize the Flask app
app = Flask(__name__)

# Set OpenAI API key
openai.api_key = 'your_openai_api_key_here'

# Initialize the LLM (OpenAI in this case)
llm = OpenAI(temperature=0.7)

# Define all your prompts

# Dataset analysis
dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and list the important features: missing values, outliers, data types, and suggest any necessary preprocessing steps. Dataset: {dataset}"
)

# Preprocessing suggestions
preprocessing_prompt = PromptTemplate(
    input_variables=["dataset_analysis"],
    template="Based on the dataset analysis: {dataset_analysis}, suggest appropriate preprocessing steps for this dataset."
)

# Outlier detection
outlier_detection_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and identify any potential outliers. Suggest methods to handle outliers in the data: {dataset}"
)

# Missing value imputation
missing_value_imputation_prompt = PromptTemplate(
    input_variables=["dataset"],
    template="Analyze the following dataset and identify columns with missing values. Suggest appropriate imputation methods for each column based on the data type: {dataset}"
)

# Initialize LLM Chains
dataset_analysis_chain = LLMChain(prompt=dataset_analysis_prompt, llm=llm)
preprocessing_chain = LLMChain(prompt=preprocessing_prompt, llm=llm)
outlier_detection_chain = LLMChain(prompt=outlier_detection_prompt, llm=llm)
missing_value_imputation_chain = LLMChain(prompt=missing_value_imputation_prompt, llm=llm)

# Combine the chains into a SequentialChain
# Each chain processes the dataset sequentially
parallel_chain = SequentialChain(
    chains=[dataset_analysis_chain, preprocessing_chain, outlier_detection_chain, missing_value_imputation_chain],
    input_variables=["dataset"],
    output_variables=["dataset_analysis", "preprocessing_suggestions", "outlier_detection", "missing_value_imputation"],
)

@app.route("/analyze_dataset", methods=["POST"])
def analyze_dataset():
    # Get the dataset from the request
    dataset = request.json.get("dataset")

    if not dataset:
        return jsonify({"error": "Dataset is required"}), 400

    # Run the parallel chain with the provided dataset
    output = parallel_chain.run(dataset=dataset)

    # Send back the results as a JSON response
    return jsonify(output)

if __name__ == "__main__":
    app.run(debug=True)

dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Analyze the following dataset: {dataset} and task type: {task_type}. Identify its nature (image, text, etc.), and suggest preprocessing steps like normalization, resizing, tokenization, etc., that could improve model accuracy."
)

hyperparameter_tuning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset}, task type: {task_type}, and a current accuracy of {accuracy_percentage}%, suggest a deep learning model architecture in Keras (such as number of layers, activation functions, dropout, optimizer) and recommend hyperparameters (e.g., number of epochs, batch size, learning rate) to improve the accuracy."
)

regenerate_code_prompt = PromptTemplate(
    input_variables=["existing_code", "dataset", "task_type", "accuracy_percentage"],
    template="""Given the existing code: {existing_code}, dataset: {dataset}, task type: {task_type}, and the current accuracy of {accuracy_percentage}%, regenerate the Keras code to improve accuracy by modifying model architecture, epochs, batch size, learning rate, and any other suitable changes."""
)

from langchain.chains import LLMChain

# Chain for analyzing dataset and suggesting preprocessing
dataset_analysis_chain = LLMChain(prompt=dataset_analysis_prompt, llm=llm)

# Chain for model and hyperparameter tuning suggestions
hyperparameter_tuning_chain = LLMChain(prompt=hyperparameter_tuning_prompt, llm=llm)

# Chain for regenerating Keras code to improve accuracy
regenerate_code_chain = LLMChain(prompt=regenerate_code_prompt, llm=llm)

dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Analyze the dataset: {dataset} and task type: {task_type}. Based on the nature of the data (images, text, etc.), suggest appropriate preprocessing techniques (e.g., resizing, normalization, augmentation, tokenization, etc.)."
)

model_architecture_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, suggest the best deep learning architecture (e.g., YOLO, ResNet, DenseNet) to improve accuracy given the current accuracy of {accuracy_percentage}%."
)

hyperparameter_optimization_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with the current model accuracy of {accuracy_percentage}%, suggest hyperparameters to tune (e.g., epochs, learning rate, batch size, optimizer) to improve model performance."
)

class_imbalance_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Given the dataset: {dataset} and task type: {task_type}, recommend techniques to address class imbalance (e.g., class weighting, oversampling, undersampling) to improve model accuracy."
)

data_augmentation_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For the dataset: {dataset} and task type: {task_type}, recommend data augmentation techniques (e.g., rotation, flipping, scaling) to improve model performance."
)

segmentation_model_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For the dataset: {dataset} and task type: {task_type} (medical image segmentation), suggest the best deep learning model (e.g., V-Net, U-Net) to improve accuracy."
)

optimizer_learning_rate_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For the dataset: {dataset} and task type: {task_type}, recommend an optimizer (e.g., Adam, SGD) and a learning rate schedule (e.g., ReduceLROnPlateau, Cyclical Learning Rates) to improve model performance."
)

transfer_learning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Given the dataset: {dataset} and task type: {task_type}, recommend a transfer learning approach using pre-trained models (e.g., ResNet, VGG) to improve model accuracy."
)

regularization_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="For the dataset: {dataset} and task type: {task_type}, suggest regularization techniques (e.g., dropout, L2 regularization, batch normalization) to improve model accuracy and prevent overfitting."
)

fine_tuning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, suggest how to fine-tune a pre-trained model (e.g., YOLO, ResNet) to improve accuracy."
)

hyperparameter_optimization_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, suggest optimal hyperparameters such as learning rate, batch size, and epochs to maximize model performance."
)

optimizer_selection_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="Given the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, recommend the best optimizer (e.g., Adam, SGD, RMSprop) and suggest hyperparameters (learning rate, momentum) to improve performance."
)

regularization_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, suggest suitable regularization techniques (e.g., dropout, L2 regularization, batch normalization) to improve accuracy and prevent overfitting."
)

model_architecture_tuning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, recommend architectural changes to improve model performance (e.g., adding layers, adjusting activation functions, changing layer sizes)."
)

lr_scheduling_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, suggest learning rate scheduling techniques (e.g., ReduceLROnPlateau, Cyclical Learning Rates) to improve model convergence."
)

data_augmentation_prompt = PromptTemplate(
    input_variables=["dataset", "task_type"],
    template="Given the dataset: {dataset} and task type: {task_type}, recommend data augmentation techniques (e.g., flipping, rotating, scaling for images or text augmentation for NLP tasks) to improve model performance."
)

transfer_learning_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, recommend using transfer learning with pre-trained models (e.g., ResNet, VGG, Inception) to improve model performance."
)

batch_normalization_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, suggest adding batch normalization layers to improve training stability and model performance."
)

early_stopping_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, recommend implementing early stopping to prevent overfitting and improve model generalization."
)

ensemble_methods_prompt = PromptTemplate(
    input_variables=["dataset", "task_type", "accuracy_percentage"],
    template="For the dataset: {dataset} and task type: {task_type}, with an accuracy of {accuracy_percentage}%, recommend ensemble methods (e.g., bagging, boosting, stacking) to combine multiple models and improve accuracy."
)

from langchain.prompts import PromptTemplate

# Refined Prompt to analyze the dataset and suggest models based on the task (classification or regression)
dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "task"],
    template="""
    Analyze the following dataset and provide insights on important features such as missing values, outliers, data types, 
    and any necessary preprocessing steps. Additionally, based on the provided task (classification or regression), 
    suggest the best machine learning models for achieving high accuracy. Consider models like Random Forest, Decision Tree, 
    Logistic Regression, Support Vector Machine, or others that might be suitable.

    Dataset: {dataset}
    Task: {task}
    """
)

dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "task"],
    template="""
    Analyze the following dataset and provide insights on important features such as missing values, outliers, data types, 
    and any necessary preprocessing steps. Also, based on the provided task (classification or regression), 
    suggest the best machine learning models for achieving high accuracy. Consider models like Random Forest, Decision Tree, 
    Logistic Regression, Support Vector Machine, or others that might be suitable.

    For the suggested models, recommend appropriate preprocessing steps such as scaling, encoding, or imputation if necessary. 
    Additionally, provide hyperparameter tuning recommendations to improve model performance and accuracy.

    Dataset: {dataset}
    Task: {task}
    """
)

from langchain.prompts import PromptTemplate

# Refined Prompt to analyze the dataset, including handling missing values, outliers, data types, preprocessing, and hyperparameter tuning
dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "task"],
    template="""
    Analyze the following dataset and provide a detailed analysis, including:
    1. **Missing Values**: Identify any columns with missing values and suggest appropriate strategies to handle them (e.g., imputation or removal).
    2. **Outliers**: Identify any potential outliers and suggest methods to handle them (e.g., removal or capping).
    3. **Data Types**: Check if the columns have appropriate data types. Suggest conversions if necessary (e.g., converting categorical features to numerical values or datetime columns).
    4. **Preprocessing Steps**: Suggest any necessary preprocessing steps based on the dataset. These may include:
        - Scaling (e.g., standardization or normalization for numerical features)
        - Encoding (e.g., one-hot encoding or label encoding for categorical variables)
        - Imputation (e.g., mean, median, or mode imputation for missing values)
        - Handling categorical features (e.g., applying ordinal encoding, one-hot encoding, or target encoding)
        - Dealing with imbalanced classes (e.g., SMOTE or class weights for classification tasks)
        - Removing or transforming skewed data if necessary.

    Based on the provided task (classification or regression), suggest the best machine learning models for achieving high accuracy. Consider models like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, or others that might be suitable.

    For the suggested models, recommend appropriate **hyperparameter tuning** options to improve model performance, such as:
    - For **Random Forest**: Number of trees, max depth, min samples split, etc.
    - For **Support Vector Machine (SVM)**: C, gamma, kernel, etc.
    - For **Logistic Regression**: Regularization strength, solver, etc.
    - For **Decision Tree**: Max depth, min samples split, etc.

    Dataset: {dataset}
    Task: {task}
    """
)

from langchain.prompts import PromptTemplate

# Refined Prompt to analyze the dataset, task type, and suggest methods for classification or regression
dataset_analysis_prompt = PromptTemplate(
    input_variables=["dataset", "taskType"],
    template="""
    Analyze the following dataset based on the task type ({taskType}) and provide a detailed analysis, including:
    1. **Missing Values**: Identify any columns with missing values and suggest appropriate strategies to handle them (e.g., imputation or removal).
    2. **Outliers**: Identify any potential outliers and suggest methods to handle them (e.g., removal or capping).
    3. **Data Types**: Check if the columns have appropriate data types. Suggest conversions if necessary (e.g., converting categorical features to numerical values or datetime columns).
    4. **Preprocessing Steps**: Suggest any necessary preprocessing steps based on the dataset and task type. These may include:
        - Scaling (e.g., standardization or normalization for numerical features)
        - Encoding (e.g., one-hot encoding or label encoding for categorical variables)
        - Imputation (e.g., mean, median, or mode imputation for missing values)
        - Handling categorical features (e.g., applying ordinal encoding, one-hot encoding, or target encoding)
        - Dealing with imbalanced classes (e.g., SMOTE or class weights for classification tasks)
        - Removing or transforming skewed data if necessary.

    Based on the provided task type ({taskType}), suggest suitable machine learning models for classification or regression. For classification, suggest models like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, etc. For regression, suggest models like Linear Regression, Decision Tree Regressor, Random Forest Regressor, etc.

    Dataset: {dataset}
    Task Type: {taskType}
    """
)

npm install react-quill axios react-chartjs-2

import React, { useState } from 'react';
import axios from 'axios';
import ReactQuill from 'react-quill'; // Text editor for code input
import 'react-quill/dist/quill.snow.css'; // React Quill styles for code

const ModelAnalysis = () => {
  const [modelCode, setModelCode] = useState('');
  const [visualizationCode, setVisualizationCode] = useState('');
  const [preprocessingCode, setPreprocessingCode] = useState('');
  const [hyperparameterTuningCode, setHyperparameterTuningCode] = useState('');
  const [suggestions, setSuggestions] = useState('');
  const [accuracy, setAccuracy] = useState(null);
  const [chartData, setChartData] = useState(null);  // For visualizing accuracy or metrics

  const handleSubmit = async () => {
    try {
      const response = await axios.post('http://localhost:5000/analyze', {
        modelCode,
        visualizationCode,
        preprocessingCode,
        hyperparameterTuningCode,
      });

      setSuggestions(response.data.suggestions);
      setAccuracy(response.data.accuracy);
      setChartData(response.data.chartData);  // Assuming chart data is returned from backend

    } catch (error) {
      console.error('Error analyzing the code:', error);
    }
  };

  return (
    <div>
      <h1>Machine Learning Model Analysis</h1>

      <div>
        <h3>Model Code</h3>
        <ReactQuill value={modelCode} onChange={setModelCode} placeholder="Paste your model code here" />
      </div>

      <div>
        <h3>Visualization Code</h3>
        <ReactQuill value={visualizationCode} onChange={setVisualizationCode} placeholder="Paste your visualization code here" />
      </div>

      <div>
        <h3>Preprocessing Code</h3>
        <ReactQuill value={preprocessingCode} onChange={setPreprocessingCode} placeholder="Paste your preprocessing code here" />
      </div>

      <div>
        <h3>Hyperparameter Tuning Code</h3>
        <ReactQuill value={hyperparameterTuningCode} onChange={setHyperparameterTuningCode} placeholder="Paste your hyperparameter tuning code here" />
      </div>

      <button onClick={handleSubmit}>Analyze and Improve</button>

      <div>
        <h3>Suggestions:</h3>
        <p>{suggestions}</p>
      </div>

      <div>
        <h3>Accuracy:</h3>
        <p>{accuracy}</p>
      </div>

      {chartData && (
        <div>
          <h3>Model Accuracy Visualization</h3>
          {/* You can use a chart library like Chart.js for visualization */}
          <Bar data={chartData} options={{ responsive: true }} />
        </div>
      )}
    </div>
  );
};

export default ModelAnalysis;

pip install flask openai langchain

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
import openai
import matplotlib.pyplot as plt
import io
import base64

app = Flask(__name__)

# Initialize OpenAI API
openai.api_key = "your-openai-api-key"
llm = OpenAI(temperature=0.5)

# Define the prompt template
model_analysis_prompt = PromptTemplate(
    input_variables=["modelCode", "visualizationCode", "preprocessingCode", "hyperparameterTuningCode"],
    template="""
    Analyze the following code sections for the provided machine learning model. Provide suggestions on how to increase accuracy and recommend improvements.
    1. **Model Code**: Are there any issues with the model? Should a different model be used?
    2. **Visualization Code**: Does the code include proper visualizations to assess model performance (accuracy, confusion matrix, etc.)?
    3. **Preprocessing Code**: Are all necessary preprocessing steps included (e.g., missing values handling, scaling, encoding)?
    4. **Hyperparameter Tuning**: Are hyperparameters being tuned adequately? Recommend values to tune or improvements in this section.

    Model Code: {modelCode}
    Visualization Code: {visualizationCode}
    Preprocessing Code: {preprocessingCode}
    Hyperparameter Tuning Code: {hyperparameterTuningCode}
    """
)

# LangChain analysis
analysis_chain = LLMChain(llm=llm, prompt=model_analysis_prompt)

# API route to analyze code
@app.route('/analyze', methods=['POST'])
def analyze_code():
    try:
        data = request.get_json()
        model_code = data['modelCode']
        visualization_code = data['visualizationCode']
        preprocessing_code = data['preprocessingCode']
        hyperparameter_tuning_code = data['hyperparameterTuningCode']

        # Use LangChain to analyze the code
        analysis_response = analysis_chain.run({
            "modelCode": model_code,
            "visualizationCode": visualization_code,
            "preprocessingCode": preprocessing_code,
            "hyperparameterTuningCode": hyperparameter_tuning_code,
        })

        # Assuming the response contains suggestions and regenerated code
        suggestions = analysis_response
        accuracy = "85%"  # This should be computed based on the model's performance in your environment
        chart_data = generate_chart_data()  # Example function for chart data

        return jsonify({
            "suggestions": suggestions,
            "accuracy": accuracy,
            "chartData": chart_data
        })

    except Exception as e:
        return jsonify({"error": str(e)})

# Generate dummy chart data (you can use actual model accuracy here)
def generate_chart_data():
    # This is just a placeholder. Replace with actual accuracy or performance metrics.
    labels = ['Model Accuracy']
    data = [85]  # Placeholder for accuracy value

    chart_data = {
        "labels": labels,
        "datasets": [
            {
                "label": 'Accuracy',
                "data": data,
                "backgroundColor": 'rgba(75, 192, 192, 0.2)',
                "borderColor": 'rgba(75, 192, 192, 1)',
                "borderWidth": 1
            }
        ]
    }

    return chart_data

if __name__ == '__main__':
    app.run(debug=True)

from langchain.prompts import PromptTemplate

# Enhanced model analysis prompt for unstructured data tasks (e.g., medical image segmentation, object detection)
advanced_model_analysis_unstructured_data_prompt = PromptTemplate(
    input_variables=["modelCode", "visualizationCode", "preprocessingCode", "hyperparameterTuningCode", "dataset", "taskType", "accuracyPercentage"],
    template="""
    Analyze the following code sections and dataset for the provided unstructured data task (e.g., medical image segmentation, object detection). Provide suggestions on how to increase accuracy and recommend improvements in the following areas:

    1. **Model Selection**: Based on the dataset: {dataset} and task type: {taskType}, is the selected model appropriate? Should a different model (e.g., V-Net, U-Net, YOLO, ResNet) be used for better performance? Consider both model architecture and task-specific nuances.

    2. **Evaluation Metrics and Performance Tracking**: Given the task type: {taskType}, suggest the best evaluation metrics to track performance, such as:
        - **Dice Coefficient**, **IoU** for segmentation and object detection tasks.
        - **F1 score**, **Precision**, **Recall** for classification tasks.
        - Track **training loss**, **validation loss**, and **accuracy** to monitor convergence.

    3. **Visualization Code**: Review the provided visualization code: {visualizationCode}. Does it include essential plots to evaluate model performance, such as accuracy, loss curves, confusion matrix, or other task-specific metrics (e.g., Dice coefficient for segmentation)? Suggest improvements or additional visualizations, like **visualizing segmentation masks** for image segmentation tasks or **bounding box plots** for object detection.

    4. **Preprocessing Code**: Evaluate the preprocessing steps in the provided code: {preprocessingCode}. Are all necessary preprocessing steps included for unstructured data (e.g., image resizing, normalization, data augmentation, handling imbalanced classes)? Are there any missing preprocessing techniques that could improve model accuracy, such as **elastic deformations** for medical images or **patch extraction** for large image datasets?

    5. **Hyperparameter Tuning**: Analyze the provided hyperparameter tuning code: {hyperparameterTuningCode}. Are the hyperparameters (learning rate, batch size, epochs, etc.) being tuned adequately? Suggest optimal values or improvements to these parameters to boost model performance. Should advanced tuning techniques like learning rate scheduling, **learning rate warm-up**, or **cyclical learning rates** be applied?

    6. **Ensemble Methods**: Suggest using **ensemble methods** to improve the model's performance, including:
        - **Model Averaging**, **Stacking**, or **Bagging** techniques to combine predictions from multiple models for better generalization and robustness.

    7. **Transfer Learning**: Recommend using pre-trained models, and explore advanced **transfer learning techniques** like:
        - **Progressive fine-tuning** of pre-trained models (e.g., fine-tuning different layers in stages).
        - **Freezing certain layers** during training to speed up convergence and then unfreezing gradually.

    8. **Regularization Techniques**: Based on the task type and current accuracy of {accuracyPercentage}%, recommend suitable regularization techniques (e.g., dropout, L2 regularization, batch normalization) to improve accuracy and prevent overfitting.

    9. **Learning Rate Finder**: Suggest using a **learning rate finder** to identify the optimal learning rate before beginning training and improving convergence.

    10. **Model Compression and Inference Optimization**: After training the model, consider applying **model compression techniques** (e.g., pruning, quantization) to reduce model size and improve inference time without sacrificing performance.

    11. **Cross-Validation**: Suggest using **k-fold cross-validation** to validate the model's performance and better tune hyperparameters across different data splits.

    Model Code: {modelCode}
    Visualization Code: {visualizationCode}
    Preprocessing Code: {preprocessingCode}
    Hyperparameter Tuning Code: {hyperparameterTuningCode}
    Dataset: {dataset}
    Task Type: {taskType}
    Accuracy Percentage: {accuracyPercentage}
    """
)

any more things is  in above template missing to generate best model or best performance and accuracy  then add in above advance template for unstructured data

import React, { useState } from 'react';
import axios from 'axios';

const CodeGenerator = () => {
  const [language, setLanguage] = useState('');
  const [task, setTask] = useState('');
  const [sampleCode, setSampleCode] = useState('');
  const [generatedCode, setGeneratedCode] = useState('');

  const languages = ['Python', 'Laravel', 'JavaScript'];
  const tasks = [
    'Remove Blank Lines',
    'Replace Text',
    'Trim Whitespace',
    'Line Numbering',
    'Remove Duplicate Lines Sort',
    'Remove Spaces Each Line',
    'Replace Space with Dash',
    'ASCII Unicode Conversion',
    'Count Words Characters',
    'Reverse Lines Words',
    'Extract Information',
    'Split Text by Characters',
    'Change Case',
    'Change Case by Find',
    'Count Words by Find',
    'Add Prefix/Suffix',
    'Add Custom Prefix/Suffix'
  ];

  const handleGenerateCode = () => {
    const payload = {
      language,
      task,
      sampleCode
    };

    axios.post('/generate_code', payload)
      .then(response => {
        setGeneratedCode(response.data.generated_code);
      })
      .catch(error => {
        console.error("There was an error generating the code!", error);
      });
  };

  return (
    <div>
      <h1>Code Generator</h1>

      <div>
        <label>Select Programming Language:</label>
        <select onChange={(e) => setLanguage(e.target.value)}>
          <option value="">Select Language</option>
          {languages.map((lang) => (
            <option key={lang} value={lang}>
              {lang}
            </option>
          ))}
        </select>
      </div>

      <div>
        <label>Select Task:</label>
        <select onChange={(e) => setTask(e.target.value)}>
          <option value="">Select Task</option>
          {tasks.map((taskOption) => (
            <option key={taskOption} value={taskOption}>
              {taskOption}
            </option>
          ))}
        </select>
      </div>

      <div>
        <label>Sample Code:</label>
        <textarea 
          value={sampleCode} 
          onChange={(e) => setSampleCode(e.target.value)} 
          rows="5" 
          cols="50" 
        />
      </div>

      <button onClick={handleGenerateCode}>Generate Code</button>

      {generatedCode && (
        <div>
          <h2>Generated Code:</h2>
          <pre>{generatedCode}</pre>
        </div>
      )}
    </div>
  );
};

export default CodeGenerator;

from flask import Flask, request, jsonify
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
import openai

app = Flask(__name__)

# Set OpenAI API key
openai.api_key = 'your_openai_api_key_here'
llm = OpenAI(temperature=0.7)

@app.route('/generate_code', methods=['POST'])
def generate_code():
    data = request.json
    language = data.get('language')
    task = data.get('task')
    sample_code = data.get('sampleCode')

    # Define task templates for different languages
    python_task_templates = {
        "Remove Blank Lines": "Write a Python function to remove blank lines from the provided code: {sample_code}",
        "Replace Text": "Write a Python function to replace text in the provided code: {sample_code}",
        # Add other tasks as needed
    }

    laravel_task_templates = {
        "Remove Blank Lines": "Write a Laravel function to remove blank lines from the provided code: {sample_code}",
        "Replace Text": "Write a Laravel function to replace text in the provided code: {sample_code}",
        # Add other tasks as needed
    }

    # Select the appropriate template based on task and language
    if language == 'Python':
        prompt_template = python_task_templates.get(task, "Task not available")
    elif language == 'Laravel':
        prompt_template = laravel_task_templates.get(task, "Task not available")
    else:
        return jsonify({"error": "Unsupported language"}), 400

    # Create a prompt using the selected task template
    prompt = PromptTemplate(input_variables=["sample_code"], template=prompt_template)

    # Generate code using LangChain
    chain = LLMChain(prompt=prompt, llm=llm)
    generated_code = chain.run(sample_code=sample_code)

    return jsonify({"generated_code": generated_code}), 200

if __name__ == "__main__"