app.py

from fastapi import FastAPI
from langserve import add_routes
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_huggingface import HuggingFacePipeline
from fastapi.middleware.cors import CORSMiddleware
import torch
import transformers

# Reduce logs
transformers.logging.set_verbosity_error()

# Create FastAPI app
app = FastAPI(
    title="Phi-3 Code Explainer API",
    description="Explains Python code using the Phi-3 Mini model and LangChain.",
    version="1.0"
)

# CORS settings
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model/tokenizer safely for CPU-only spaces
model_id = "microsoft/phi-3-mini-4k-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# If GPU available use float16, else default
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)

# Generation pipeline
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=256,
    return_full_text=False,
    do_sample=True,
    temperature=0.7
)

# LangChain pipeline
llm = HuggingFacePipeline(pipeline=generator)
prompt = ChatPromptTemplate.from_template(
    """Instruction:
Explain this Python code step-by-step:
Use code with caution
{code}

Explanation:"""
)

parser = StrOutputParser()
chain = prompt | llm | parser

# Add route
add_routes(app, chain, path="/explain")

# Run server (necessary for Hugging Face Spaces)
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=7860)