Update model.py

model.py

@@ -12,6 +12,7 @@ from nltk.tokenize import sent_tokenize
 from transformers import pipeline
 import numpy as np
 import logging
+import re
 
 # === Pipelines ===
 summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
@@ -60,7 +61,7 @@ def detect_emotion(text):
         logging.warning(f"Emotion detection failed: {e}")
         return "neutral"
 
-# === Follow-up Q&A (Flexible for list or str) ===
+# === Follow-up Q&A ===
 def answer_followup(text, question, verbosity="brief"):
     try:
         if isinstance(question, list):
@@ -68,10 +69,7 @@ def answer_followup(text, question, verbosity="brief"):
             for q in question:
                 response = qa_pipeline({"question": q, "context": text})
                 ans = response.get("answer", "")
-                if verbosity.lower() == "detailed":
-                    answers.append(f"**{q}** → {ans}")
-                else:
-                    answers.append(ans)
+                answers.append(f"**{q}** → {ans}" if verbosity.lower() == "detailed" else ans)
             return answers
         else:
             response = qa_pipeline({"question": question, "context": text})
@@ -81,7 +79,6 @@ def answer_followup(text, question, verbosity="brief"):
         logging.warning(f"Follow-up error: {e}")
         return "Sorry, I couldn't generate a follow-up answer."
 
-# === Fast follow-up (used for direct /followup route) ===
 def answer_only(text, question):
     try:
         if not question:
@@ -91,7 +88,7 @@ def answer_only(text, question):
         logging.warning(f"Answer-only failed: {e}")
         return "Q&A failed."
 
-# === Optional Explanation Generator ===
+# === Explanation Generator ===
 def generate_explanation(text):
     try:
         explanation = summarizer(text, max_length=60, min_length=20, do_sample=False)[0]["summary_text"]
@@ -100,44 +97,43 @@ def generate_explanation(text):
         logging.warning(f"Explanation failed: {e}")
         return "⚠️ Explanation could not be generated."
 
+# === Churn Risk Estimator ===
+def assess_churn_risk(sentiment_label, emotion_label):
+    if sentiment_label.lower() == "negative" and emotion_label.lower() in ["anger", "fear", "sadness", "frustrated"]:
+        return "High Risk"
+    return "Low Risk"
+
+# === Pain Point Extractor ===
+def extract_pain_points(text):
+    common_issues = [
+        "slow", "crash", "lag", "expensive", "confusing", "noisy", "poor", "rude",
+        "unhelpful", "bug", "broken", "unresponsive", "not working", "error", "delay", "disconnect"
+    ]
+    matched = [kw for kw in common_issues if re.search(rf"\b{kw}\b", text.lower())]
+    return list(set(matched))[:5]
+
 # === Industry Detector ===
 def detect_industry(text):
     text = text.lower()
-    if any(k in text for k in ["doctor", "hospital", "health", "pill", "med"]):
-        return "Healthcare"
-    if any(k in text for k in ["flight", "hotel", "trip", "booking"]):
-        return "Travel"
-    if any(k in text for k in ["bank", "loan", "credit", "payment"]):
-        return "Banking"
-    if any(k in text for k in ["gym", "trainer", "fitness", "workout"]):
-        return "Fitness"
-    if any(k in text for k in ["movie", "series", "stream", "video"]):
-        return "Entertainment"
-    if any(k in text for k in ["game", "gaming", "console"]):
-        return "Gaming"
-    if any(k in text for k in ["food", "delivery", "restaurant", "order"]):
-        return "Food Delivery"
-    if any(k in text for k in ["school", "university", "teacher", "course"]):
-        return "Education"
-    if any(k in text for k in ["insurance", "policy", "claim"]):
-        return "Insurance"
-    if any(k in text for k in ["property", "rent", "apartment", "house"]):
-        return "Real Estate"
-    if any(k in text for k in ["shop", "buy", "product", "phone", "amazon", "flipkart"]):
-        return "E-commerce"
+    if any(k in text for k in ["doctor", "hospital", "health", "pill", "med"]): return "Healthcare"
+    if any(k in text for k in ["flight", "hotel", "trip", "booking"]): return "Travel"
+    if any(k in text for k in ["bank", "loan", "credit", "payment"]): return "Banking"
+    if any(k in text for k in ["gym", "trainer", "fitness", "workout"]): return "Fitness"
+    if any(k in text for k in ["movie", "series", "stream", "video"]): return "Entertainment"
+    if any(k in text for k in ["game", "gaming", "console"]): return "Gaming"
+    if any(k in text for k in ["food", "delivery", "restaurant", "order"]): return "Food Delivery"
+    if any(k in text for k in ["school", "university", "teacher", "course"]): return "Education"
+    if any(k in text for k in ["insurance", "policy", "claim"]): return "Insurance"
+    if any(k in text for k in ["property", "rent", "apartment", "house"]): return "Real Estate"
+    if any(k in text for k in ["shop", "buy", "product", "phone", "amazon", "flipkart"]): return "E-commerce"
     return "Generic"
 
 # === Product Category Detector ===
 def detect_product_category(text):
     text = text.lower()
-    if any(k in text for k in ["mobile", "smartphone", "iphone", "samsung", "phone"]):
-        return "Mobile Devices"
-    if any(k in text for k in ["laptop", "macbook", "notebook", "chromebook"]):
-        return "Laptops"
-    if any(k in text for k in ["tv", "refrigerator", "microwave", "washer"]):
-        return "Home Appliances"
-    if any(k in text for k in ["watch", "band", "fitbit", "wearable"]):
-        return "Wearables"
-    if any(k in text for k in ["app", "portal", "site", "website"]):
-        return "Web App"
+    if any(k in text for k in ["mobile", "smartphone", "iphone", "samsung", "phone"]): return "Mobile Devices"
+    if any(k in text for k in ["laptop", "macbook", "notebook", "chromebook"]): return "Laptops"
+    if any(k in text for k in ["tv", "refrigerator", "microwave", "washer"]): return "Home Appliances"
+    if any(k in text for k in ["watch", "band", "fitbit", "wearable"]): return "Wearables"
+    if any(k in text for k in ["app", "portal", "site", "website"]): return "Web App"
     return "General"