app.py

from fastapi import FastAPI
from transformers import pipeline
import torch
from pydantic import BaseModel
from typing import List, Dict
if torch.backends.mps.is_available():
    device = torch.device("mps")
elif torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")
print(device)

app = FastAPI()

modelName = "Qwen/Qwen2.5-1.5B-Instruct" #Qwen/Qwen2.5-1.5B-Instruct 
pipe = pipeline("text-generation", model=modelName, device=device, batch_size=8)
sentiment_model = pipeline("sentiment-analysis", device=device)

class ChatRequest(BaseModel):
    conversationHistory: List[Dict[str, str]]



@app.get("/")
async def root():
    return {"message": "Hello World"}

# NOTE - we configure docs_url to serve the interactive Docs at the root path
# of the app. This way, we can use the docs as a landing page for the app on Spaces.
# app = FastAPI(docs_url="/")

@app.get("/generate")
def generate(text: str):
    """
    Generate response.
    """
    content = [{"role": "user", "content": text}]
    output = pipe(content, num_return_sequences=1, max_new_tokens=250)
    
    # print(output)
    
    print(output)
    return {"output": output[0]["generated_text"][-1]['content']}

@app.post("/chat")
def chat(request: ChatRequest):
    """
    Generate reposnse form the NLP Model.
    """
    
    output = pipe(request.conversationHistory, num_return_sequences=1, max_new_tokens=250)
    return output