Delete model.py

model.py

@@ -1,143 +0,0 @@
-import os
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
-os.environ["HF_HOME"] = "/tmp/hf-home"
-
-import nltk
-nltk.download("punkt", download_dir="/tmp/nltk_data")
-
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.cluster import KMeans
-from sklearn.metrics.pairwise import cosine_similarity
-from nltk.tokenize import sent_tokenize
-from transformers import pipeline
-import numpy as np
-import logging
-
-# === Pipelines ===
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
-qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
-emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=1)
-
-# === Brief Summarization ===
-def summarize_review(text, max_len=80, min_len=20):
-    try:
-        return summarizer(text, max_length=max_len, min_length=min_len, do_sample=False)[0]["summary_text"]
-    except Exception as e:
-        logging.warning(f"Summarization fallback used: {e}")
-        return text
-
-# === Smart Summarization with Clustering ===
-def smart_summarize(text, n_clusters=1):
-    try:
-        sentences = sent_tokenize(text)
-        if len(sentences) <= 1:
-            return text
-        tfidf = TfidfVectorizer(stop_words="english")
-        tfidf_matrix = tfidf.fit_transform(sentences)
-        if len(sentences) <= n_clusters:
-            return " ".join(sentences)
-        kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(tfidf_matrix)
-        summary_sentences = []
-        for i in range(n_clusters):
-            idx = np.where(kmeans.labels_ == i)[0]
-            if not len(idx):
-                continue
-            avg_vector = np.asarray(tfidf_matrix[idx].mean(axis=0))
-            sim = cosine_similarity(avg_vector, tfidf_matrix[idx].toarray())
-            most_representative = sentences[idx[np.argmax(sim)]]
-            summary_sentences.append(most_representative)
-        return " ".join(sorted(summary_sentences, key=sentences.index))
-    except Exception as e:
-        logging.error(f"Smart summarize error: {e}")
-        return text
-
-# === Emotion Detection ===
-def detect_emotion(text):
-    try:
-        result = emotion_pipeline(text)[0]
-        return result["label"]
-    except Exception as e:
-        logging.warning(f"Emotion detection failed: {e}")
-        return "neutral"
-
-# === Follow-up Q&A (Flexible for list or str) ===
-def answer_followup(text, question, verbosity="brief"):
-    try:
-        if isinstance(question, list):
-            answers = []
-            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)
-            return answers
-        else:
-            response = qa_pipeline({"question": question, "context": text})
-            ans = response.get("answer", "")
-            return f"**{question}** → {ans}" if verbosity.lower() == "detailed" else ans
-    except Exception as e:
-        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:
-            return "No question provided."
-        return qa_pipeline({"question": question, "context": text}).get("answer", "No answer found.")
-    except Exception as e:
-        logging.warning(f"Answer-only failed: {e}")
-        return "Q&A failed."
-
-# === Optional Explanation Generator ===
-def generate_explanation(text):
-    try:
-        explanation = summarizer(text, max_length=60, min_length=20, do_sample=False)[0]["summary_text"]
-        return f"🧠 This review can be explained as: {explanation}"
-    except Exception as e:
-        logging.warning(f"Explanation failed: {e}")
-        return "⚠️ Explanation could not be generated."
-
-# === 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"
-    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"
-    return "General"