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import os
import torch
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import uvicorn
# Define a Pydantic model for request validation
class Query(BaseModel):
text: str
# Initialize FastAPI app
app = FastAPI(title="Financial Chatbot API")
# Load your fine-tuned model and tokenizer using the updated model name
model_name = "Phoenix21/meta-llama-Llama-3.2-3B-2025-03-13-checkpoints"
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
# Create a text-generation pipeline
chat_pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256,
temperature=0.7,
top_p=0.95,
)
# Define an endpoint for generating responses
@app.post("/generate")
def generate(query: Query):
prompt = f"Question: {query.text}\nAnswer: "
response = chat_pipe(prompt)[0]["generated_text"]
return {"response": response}
# Run the app using uvicorn; default port is 7860 (as expected by Hugging Face Spaces)
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860))
uvicorn.run(app, host="0.0.0.0", port=port)
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