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import os
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from typing import Dict, Any
import json
def convert_code(input_code: str, model_name: str = "llama-3.3-70b-versatile") -> Dict[str, str]:
"""
Converts input Python/IPYNB code into a Gradio app structure.
Returns a dictionary containing app.py, requirements.txt, and README.md content.
"""
# Check for API Key
if not os.environ.get("GROQ_API_KEY"):
raise ValueError("GROQ_API_KEY environment variable is not set.")
chat = ChatGroq(temperature=0, model_name=model_name)
system_prompt = """You are an expert Python developer specializing in Gradio and Hugging Face Spaces.
Your task is to convert the provided Python code (which might be a script or a notebook content) into a deployable Gradio web application.
You must output a JSON object with exactly three keys:
1. "app_py": The complete code for app.py. It must use Gradio to create a UI for the functionality in the source code. Ensure all imports are correct.
2. "requirements_txt": A list of dependencies required to run the app. Include 'gradio'.
3. "readme_md": A README.md file customized for a Hugging Face Space.
Rules for app.py:
- Encapsulate logic in functions.
- Create a professional Gradio interface `demo = gr.Interface(...)` or `with gr.Blocks() as demo: ...`.
- Ensure `demo.launch()` is called at the end if it's main, but standard HF spaces just look for `demo` object or run the script.
- Handle potential errors gracefully.
Do not include markdown triple backticks in the JSON values. The values should be raw string content.
"""
human_template = "Convert this code into a Gradio app:\n\n{code}"
prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
("human", human_template)
])
chain = prompt | chat | JsonOutputParser()
try:
result = chain.invoke({"code": input_code})
return result
except Exception as e:
# Fallback or error handling
raise RuntimeError(f"Failed to generate code: {str(e)}")
def parse_notebook(notebook_content: dict) -> str:
"""Extracts code cells from a notebook dictionary."""
code = []
for cell in notebook_content.get('cells', []):
if cell.get('cell_type') == 'code':
code.append("".join(cell.get('source', [])))
return "\n\n".join(code)
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