app.py

import gradio as gr
from huggingface_hub import InferenceClient
import sys
import io
import traceback
import re  # Import the regular expressions module

# Initialize the AI model
model_name = "Qwen/Qwen2.5-72B-Instruct"
client = InferenceClient(model_name)

def llm_inference(messages):
    eos_token = "<|endoftext|>"
    output = client.chat.completions.create(
        messages=messages,
        stream=False,
        temperature=0.7,
        top_p=0.1,
        max_tokens=412,
        stop=[eos_token]
    )
    response = ''
    for choice in output.choices:
        response += choice['message']['content']
    return response

def execute_code(code):
    old_stdout = sys.stdout
    redirected_output = sys.stdout = io.StringIO()
    try:
        exec(code, {})
        output = redirected_output.getvalue()
    except Exception as e:
        output = f"Error: {e}\n{traceback.format_exc()}"
    finally:
        sys.stdout = old_stdout
    return output

def is_math_task(user_input):
    """
    Simple heuristic to determine if the user input is a math task.
    This can be enhanced with more sophisticated methods or NLP techniques.
    """
    math_keywords = [
        'calculate', 'compute', 'solve', 'integrate', 'differentiate',
        'derivative', 'integral', 'factorial', 'sum', 'product',
        'average', 'mean', 'median', 'mode', 'variance', 'standard deviation',
        'limit', 'matrix', 'determinant', 'equation', 'expression'
    ]
    operators = ['+', '-', '*', '/', '^', '**', 'sqrt', 'sin', 'cos', 'tan', 'log', 'exp']
    user_input_lower = user_input.lower()
    return any(keyword in user_input_lower for keyword in math_keywords) or any(op in user_input for op in operators)

def chat(user_input, history):
    """
    Handles the chat interaction. If the user input is detected as a math task,
    it performs a two-step process:
    1. Generates an explanation of how to solve the task.
    2. Generates Python code based on the explanation and executes it.
    """
    if is_math_task(user_input):
        # Step 1: Generate Explanation
        explanation_messages = [
            {
                "role": "system",
                "content": "Provide a very small explanation on how to approach the following mathematical task without calculating the answer."
            },
            {
                "role": "user",
                "content": f"Provide a short explanation on how to solve the following mathematical problem: {user_input}"
            },
        ]
        explanation = llm_inference(explanation_messages)
        
        # Step 2: Generate Python Code using Explanation and User Prompt
        code_prompt = f"Based on the following explanation, write a Python program to solve the mathematical task using Python. Ensure that the program includes a print statement to output the answer. Write only code. Writing any comments or anything else is prohibited. \n\nExplanation: {explanation}\n\nTask: {user_input}"
        
        code_messages = [
            {
                "role": "system",
                "content": "You are a Python developer. Write Python code based on the provided explanation and task."
            },
            {
                "role": "user",
                "content": f"{code_prompt}"
            },
        ]
        generated_code = llm_inference(code_messages)
        
        # Strip code tags using regex
        # This regex removes ```python and ``` or any other markdown code fences
        cleaned_code = re.sub(r"```(?:python)?\n?", "", generated_code).strip()
        cleaned_code = re.sub(r"```", "", cleaned_code).strip()
        
        # Execute the cleaned code
        execution_result = execute_code(cleaned_code)
        
        # Prepare the assistant response
        assistant_response = (
            f"**Explanation:**\n{explanation}\n\n"
            f"**Generated Python Code:**\n```python\n{cleaned_code}\n```\n\n"
            f"**Execution Result:**\n```\n{execution_result}\n```"
        )
    else:
        # For regular chat messages, use the AI's response
        messages = [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": user_input
            },
        ]
        assistant_response = llm_inference(messages)
    
    # Append to chat history
    history.append((user_input, assistant_response))
    return history, history

with gr.Blocks() as demo:
    gr.Markdown("# 🐍 Python Helper Chatbot")
    with gr.Tab("Chat"):
        chatbot = gr.Chatbot()
        msg = gr.Textbox(placeholder="Type your message here...", label="Your Message")
        msg.submit(chat, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
    
    with gr.Tab("Interpreter"):
        gr.Markdown("### 🖥️ Test Your Code")
        code_input = gr.Code(language="python", label="Python Code Input")
        run_button = gr.Button("Run Code")
        code_output = gr.Textbox(label="Output", lines=10)
        run_button.click(execute_code, inputs=code_input, outputs=code_output)
    
    with gr.Tab("Logs"):
        gr.Markdown("### 📜 Logs")
        log_output = gr.Textbox(label="Logs", lines=10, interactive=False)

# Launch the Gradio app
demo.launch()