app.py

from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from transformers import pipeline
import os
import uvicorn

app = FastAPI()

# Mount the templates directory for serving HTML
templates = Jinja2Templates(directory="templates")

# Load the zero-shot classification model
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Route to serve index.html
@app.get("/", response_class=HTMLResponse)
async def index(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

# Route to handle text classification requests
@app.post("/classify")
async def classify_text(data: dict):
    try:
        text = data.get("document")
        labels = data.get("labels")

        if not text or not labels:
            return {"error": "Please provide both text and labels"}, 400

        # Perform classification
        result = classifier(text, labels, multi_label=False)
        response = {
            "labels": result["labels"],
            "scores": result["scores"]
        }
        return response, 200
    except Exception as e:
        return {"error": str(e)}, 500

# Run the app on HF中国镜像站's required port
if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)