Update main.py

main.py

@@ -5,12 +5,12 @@ from fastapi.openapi.docs import get_swagger_ui_html
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from transformers import pipeline
-import logging, traceback
-from model import summarize_review, smart_summarize
+import os, logging, traceback
+from model import summarize_review, smart_summarize, detect_industry, detect_product_category, answer_followup
 from typing import Optional, List
 
 app = FastAPI(
-    title="🧠 NeuroPulse AI",
+    title="\U0001f9e0 NeuroPulse AI",
     description="Multilingual GenAI for smarter feedback — summarization, sentiment, emotion, aspects, Q&A and tags.",
     version="2025.1.0",
     openapi_url="/openapi.json",
@@ -35,7 +35,7 @@ async def exception_handler(request: Request, exc: Exception):
 def custom_swagger_ui():
     return get_swagger_ui_html(
         openapi_url=app.openapi_url,
-        title="🧠 Swagger UI - NeuroPulse AI",
+        title="\U0001f9e0 Swagger UI - NeuroPulse AI",
         swagger_favicon_url="https://cdn-icons-png.flaticon.com/512/3794/3794616.png",
         swagger_js_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui-bundle.js",
         swagger_css_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui.css",
@@ -52,6 +52,7 @@ class ReviewInput(BaseModel):
     aspects: bool = False
     follow_up: Optional[str] = None
     product_category: Optional[str] = None
+    device: Optional[str] = None
     intelligence: Optional[bool] = False
     verbosity: Optional[str] = "detailed"
     explain: Optional[bool] = False
@@ -62,40 +63,48 @@ class BulkReviewInput(BaseModel):
     industry: Optional[List[str]] = None
     aspects: bool = False
     product_category: Optional[List[str]] = None
-    intelligence: Optional[bool] = False
+    device: Optional[List[str]] = None
 
 VALID_API_KEY = "my-secret-key"
 logging.basicConfig(level=logging.INFO)
 sentiment_pipeline = pipeline("sentiment-analysis")
 
-def auto_fill(value: Optional[str], default: str = "Generic") -> str:
-    if not value or value.strip().lower() == "auto-detect":
-        return default
-    return value
-
 @app.post("/analyze/")
 async def analyze(data: ReviewInput, x_api_key: str = Header(None)):
     if x_api_key != VALID_API_KEY:
         raise HTTPException(status_code=401, detail="❌ Unauthorized: Invalid API key")
-
     if len(data.text.split()) < 10:
         raise HTTPException(status_code=400, detail="⚠️ Review too short for analysis (min. 10 words).")
 
     try:
-        summary = smart_summarize(data.text) if data.intelligence else summarize_review(data.text)
+        # Smart summary logic based on verbosity and intelligence
         if data.verbosity.lower() == "brief":
-            summary = summary.split(".")[0].strip() + "."
+            summary = summarize_review(data.text)
+        else:
+            summary = smart_summarize(data.text, n_clusters=2 if data.intelligence else 1)
 
         sentiment = sentiment_pipeline(data.text)[0]
+        emotion = "joy"  # Placeholder
+
+        # Auto-detection logic
+        industry = data.industry if data.industry and data.industry.lower() != "auto-detect" else detect_industry(data.text)
+        product_category = data.product_category if data.product_category and data.product_category.lower() != "auto-detect" else detect_product_category(data.text)
+        device = "Web"
+
+        follow_up_response = None
+        if data.follow_up:
+            follow_up_response = answer_followup(data.text, data.follow_up)
 
         return {
             "summary": summary,
             "sentiment": sentiment,
-            "emotion": "joy",  # placeholder
-            "product_category": auto_fill(data.product_category, "General"),
-            "industry": auto_fill(data.industry, "Generic")
+            "emotion": emotion,
+            "product_category": product_category,
+            "device": device,
+            "industry": industry,
+            "follow_up": follow_up_response
         }
 
-    except Exception:
+    except Exception as e:
         logging.error(f"🔥 Unexpected analysis failure: {traceback.format_exc()}")
-        raise HTTPException(status_code=500, detail="Internal Server Error during analysis. Please contact support.")
+        raise HTTPException(status_code=500, detail="Internal Server Error during analysis. Please contact support.")