| from fastapi import FastAPI |
| from pydantic import BaseModel, Field |
| from fastapi.middleware.cors import CORSMiddleware |
| import logging |
| from model.argq import ArgqClassifier |
| from datetime import datetime |
| import firebase_admin |
| from firebase_admin import credentials, firestore |
| import uvicorn |
| from os import getenv, path |
|
|
|
|
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') |
|
|
| app = FastAPI(title="ArgQ Backend", version="0.0.1") |
|
|
| app.add_middleware( |
| CORSMiddleware, |
| allow_origins=["*"], |
| allow_credentials=True, |
| allow_methods=["*"], |
| allow_headers=["*"], |
| ) |
|
|
| logging.info("Starting application") |
| cred_file_path = path.join(path.dirname(__file__), "../credentials/firebase-adminsdk.json") |
| cred = credentials.Certificate(cred_file_path) |
| firebase_admin.initialize_app(cred) |
|
|
| db = firestore.client() |
|
|
| logging.info("Loading model..") |
| model = ArgqClassifier() |
| logging.info("Model loaded") |
|
|
| class Tweet(BaseModel): |
| text: str |
|
|
| class TextWithAspects(BaseModel): |
| tweet: Tweet |
| aspects: list = ["quality", "clarity", "organization", "credibility", "emotional_polarity", "emotional_intensity"] |
|
|
| class FeedbackItem(BaseModel): |
| email: str |
| text: str |
| timestamp: datetime = Field(default_factory=datetime.utcnow) |
|
|
| @app.post("/argq/classify") |
| async def get_text_classification(tweet: Tweet): |
| classification = await model.classify_text(tweet.text) |
| return { |
| "classification": classification |
| } |
|
|
| @app.post("/argq/classify/aspects") |
| async def get_text_classification_by_aspects(request: TextWithAspects): |
| classification = { |
| aspect: await model.classify_text_by_aspect(request.tweet.text, aspect) for aspect in request.aspects |
| } |
| return { |
| "classification": classification |
| } |
|
|
| @app.post("/argq/feedback") |
| async def post_feedback(item: FeedbackItem): |
| feedback_data = item.model_dump() |
| now = datetime.now() |
| doc_name = now.strftime("%Y%m%d_%H%M%S") |
| doc_ref = db.collection('feedback').document(doc_name) |
| doc_ref.set(feedback_data) |
| return {"status": "success", "feedback_received": feedback_data} |
|
|
| if __name__ == "__main__": |
| uvicorn.run(app, host="0.0.0.0", port=int(getenv("PORT", 8000)), reload=True) |
