import os 
import gradio as gr
from huggingface_hub import InferenceClient
hfapi_token=os.getenv('hf_api_token')
# ✅ Add your Hugging Face API token here
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hfapi_token)

def explain_and_run_code(code_snippet):
    system_message = "You are an expert assistant that explains Python code snippets clearly,shortly and concisely with the output."
    explanation = ""
    output = ""

    try:
        messages = [{"role": "system", "content": system_message}]
        messages.append({
            "role": "user",
            "content": f"Please provide a detailed explanation of the following code snippet:\n```{code_snippet}```"
        })

        for msg in client.chat_completion(
            messages,
            max_tokens=2047,
            stream=True,
            temperature=0.7,
            top_p=0.95
        ):
            token = msg["choices"][0]["delta"]["content"]
            explanation += token
    except Exception as e:
        explanation = f"An error occurred during explanation: {str(e)}"

    try:
        import io
        import contextlib
        output_buffer = io.StringIO()
        with contextlib.redirect_stdout(output_buffer):
            exec(code_snippet)
        output = output_buffer.getvalue()
    except Exception as e:
        output = f"An error occurred while running the code: {str(e)}"

    return f"**Explanation:**\n{explanation}\n\n**Output:**\n{output}"


demo = gr.Interface(
    fn=explain_and_run_code,
    inputs=gr.Textbox(placeholder="Enter your code snippet here...", label="Code Snippet", lines=10),
    outputs=gr.Textbox(label="Explanation and Output", lines=15),
    title="DECIPHER The Python Code Explainer\n\n                    AI Capstone Project\n                         (XII-C)",
    theme="default"
)

if __name__ == "__main__":
    demo.launch()