Update main.py

main.py

@@ -1,7 +1,8 @@
-from fastapi import FastAPI, Request, Header, HTTPException
-from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
+from fastapi import FastAPI, Request, Header, HTTPException, UploadFile, File
+from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.openapi.utils import get_openapi
 from fastapi.openapi.docs import get_swagger_ui_html
+from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from transformers import pipeline
 from io import StringIO
@@ -19,6 +20,14 @@ app = FastAPI(
     redoc_url="/redoc"
 )
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 @app.get("/docs", include_in_schema=False)
 def custom_swagger_ui():
     return get_swagger_ui_html(
@@ -97,7 +106,6 @@ def root():
     </html>
     """
 
-# --- Models ---
 class ReviewInput(BaseModel):
     text: str
     model: str = "distilbert-base-uncased-finetuned-sst-2-english"
@@ -123,11 +131,9 @@ class TranslationInput(BaseModel):
     text: str
     target_lang: str = "fr"
 
-# --- Auth & Logging ---
 VALID_API_KEY = "my-secret-key"
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
 
-# --- Load Models Once ---
 summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
 sentiment_pipelines = {
@@ -135,7 +141,6 @@ sentiment_pipelines = {
     "nlptown/bert-base-multilingual-uncased-sentiment": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
 }
 
-# --- Analyze (Bulk) ---
 @app.post("/bulk/")
 async def bulk(data: BulkReviewInput, x_api_key: str = Header(None)):
     if x_api_key != VALID_API_KEY:
@@ -167,6 +172,14 @@ async def bulk(data: BulkReviewInput, x_api_key: str = Header(None)):
 
     return {"results": results}
 
+@app.post("/upload/")
+async def upload(file: UploadFile = File(...)):
+    content = await file.read()
+    decoded = content.decode("utf-8")
+    reader = csv.DictReader(StringIO(decoded))
+    reviews = [row["review"] for row in reader if "review" in row]
+    return {"count": len(reviews), "sample": reviews[:3]}
+
 @app.post("/analyze/")
 async def analyze(request: Request, data: ReviewInput, x_api_key: str = Header(None), download: str = None):
     if x_api_key != VALID_API_KEY:
@@ -205,13 +218,12 @@ async def analyze(request: Request, data: ReviewInput, x_api_key: str = Header(N
         "device": data.device,
         "industry": data.industry
     }
-# --- Translate ---
+
 @app.post("/translate/")
 async def translate(data: TranslationInput):
     translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{data.target_lang}")
     return {"translated_text": translator(data.text)[0]["translation_text"]}
 
-# --- LLM Agent Chat ---
 @app.post("/chat/")
 async def chat(input: ChatInput, x_api_key: str = Header(None)):
     if x_api_key != VALID_API_KEY:
@@ -229,7 +241,6 @@ def chat_llm(question, context):
     )
     return res.choices[0].message.content.strip()
 
-# --- Custom OpenAPI ---
 def custom_openapi():
     if app.openapi_schema:
         return app.openapi_schema
@@ -246,4 +257,4 @@ Summarize reviews, detect sentiment/emotion, extract aspects, tag metadata, and
     app.openapi_schema = openapi_schema
     return app.openapi_schema
 
-app.openapi = custom_openapi
+app.openapi = custom_openapi