app.py

import os
import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel
import uvicorn

from huggingface_hub import login

# Authenticate with Hugging Face Hub using the HF_TOKEN environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
    login(token=HF_TOKEN)
else:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")

# Define a Pydantic model for request validation
class Query(BaseModel):
    text: str

app = FastAPI(title="Financial Chatbot API")

# Load the base model from Meta-Llama
base_model_name = "meta-llama/Llama-3.2-3B"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    trust_remote_code=True
)

# Load the finetuned adapter using PEFT
peft_model_id = "Phoenix21/llama-3-2-3b-finetuned-finance_checkpoint2"
model = PeftModel.from_pretrained(base_model, peft_model_id)

# Load the tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

# Create a text-generation pipeline using the loaded model and tokenizer
chat_pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.95,
)

@app.post("/generate")
def generate(query: Query):
    prompt = f"Question: {query.text}\nAnswer: "
    response = chat_pipe(prompt)[0]["generated_text"]
    return {"response": response}

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)