Update model.py

model.py

@@ -1,121 +1,122 @@
-from typing import List, Optional
-from pydantic import BaseModel
-from transformers import pipeline
-import nltk.data
-
-# ✅ Extra: Smart Summarization Imports
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.cluster import KMeans
-from nltk.tokenize import sent_tokenize
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-
-# 📄 Load HuggingFace Pipelines
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
-sentiment_analyzer = pipeline("sentiment-analysis")
-
-# 🧠 Basic Summarization (Abstractive)
-def summarize_review(text):
-    return summarizer(text, max_length=60, min_length=10, do_sample=False, no_repeat_ngram_size=3)[0]["summary_text"]
-
-# 🧠 Smart Summarization (Clustered Key Sentences)
-def smart_summarize(text, n_clusters=1):
-    """Improved summarization using clustering on sentence embeddings"""
-    tokenizer = nltk.tokenize.PunktSentenceTokenizer()  # ✅ Use default trained Punkt tokenizer
-    sentences = tokenizer.tokenize(text)
-
-    if len(sentences) <= 1:
-        return text
-
-    vectorizer = TfidfVectorizer(stop_words="english")
-    tfidf_matrix = vectorizer.fit_transform(sentences)
-
-    if len(sentences) <= n_clusters:
-        return " ".join(sentences)
-
-    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
-    kmeans.fit(tfidf_matrix)
-
-    avg = []
-    for i in range(n_clusters):
-        idx = np.where(kmeans.labels_ == i)[0]
-        if len(idx) == 0:
-            continue
-        avg_vector = tfidf_matrix[idx].mean(axis=0).A1.reshape(1, -1)  # Convert np.matrix to ndarray
-        sim = cosine_similarity(avg_vector, tfidf_matrix[idx])
-        most_representative_idx = idx[np.argmax(sim)]
-        avg.append(sentences[most_representative_idx])
-
-    return " ".join(sorted(avg, key=sentences.index))
-
-# 📊 Sentiment Detection
-def analyze_sentiment(text):
-    result = sentiment_analyzer(text)[0]
-    label = result["label"]
-    score = result["score"]
-
-    if "star" in label:
-        stars = int(label[0])
-        if stars <= 2:
-            label = "NEGATIVE"
-        elif stars == 3:
-            label = "NEUTRAL"
-        else:
-            label = "POSITIVE"
-
-    return {
-        "label": label,
-        "score": score
-    }
-
-# 🔥 Emotion Detection (heuristic-based)
-def detect_emotion(text):
-    text_lower = text.lower()
-    if "angry" in text_lower or "hate" in text_lower:
-        return "anger"
-    elif "happy" in text_lower or "love" in text_lower:
-        return "joy"
-    elif "sad" in text_lower or "disappointed" in text_lower:
-        return "sadness"
-    elif "confused" in text_lower or "unclear" in text_lower:
-        return "confusion"
-    else:
-        return "neutral"
-
-# 🧩 Aspect-Based Sentiment (mock)
-def extract_aspect_sentiment(text, aspects: list):
-    results = {}
-    text_lower = text.lower()
-    for asp in aspects:
-        label = "positive" if asp in text_lower and "not" not in text_lower else "neutral"
-        results[asp] = {
-            "label": label,
-            "confidence": 0.85
-        }
-    return results
-
-# ✅ Pydantic Schemas for FastAPI
-class ReviewInput(BaseModel):
-    text: str
-    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
-    industry: str = "Generic"
-    aspects: bool = False
-    follow_up: Optional[str] = None
-    product_category: Optional[str] = None
-    device: Optional[str] = None
-
-class BulkReviewInput(BaseModel):
-    reviews: List[str]
-    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
-    industry: str = "Generic"
-    aspects: bool = False
-    product_category: Optional[str] = None
-    device: Optional[str] = None
-
-class TranslationInput(BaseModel):
-    text: str
-    target_lang: str = "fr"
-
-class ChatInput(BaseModel):
-    question: str
+from typing import List, Optional
+from pydantic import BaseModel
+from transformers import pipeline
+import nltk.data
+
+# ✅ Extra: Smart Summarization Imports
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.cluster import KMeans
+from nltk.tokenize import sent_tokenize
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import os
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
+# 📄 Load HuggingFace Pipelines
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# 🧠 Basic Summarization (Abstractive)
+def summarize_review(text):
+    return summarizer(text, max_length=60, min_length=10, do_sample=False, no_repeat_ngram_size=3)[0]["summary_text"]
+
+# 🧠 Smart Summarization (Clustered Key Sentences)
+def smart_summarize(text, n_clusters=1):
+    """Improved summarization using clustering on sentence embeddings"""
+    tokenizer = nltk.tokenize.PunktSentenceTokenizer()  # ✅ Use default trained Punkt tokenizer
+    sentences = tokenizer.tokenize(text)
+
+    if len(sentences) <= 1:
+        return text
+
+    vectorizer = TfidfVectorizer(stop_words="english")
+    tfidf_matrix = vectorizer.fit_transform(sentences)
+
+    if len(sentences) <= n_clusters:
+        return " ".join(sentences)
+
+    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
+    kmeans.fit(tfidf_matrix)
+
+    avg = []
+    for i in range(n_clusters):
+        idx = np.where(kmeans.labels_ == i)[0]
+        if len(idx) == 0:
+            continue
+        avg_vector = tfidf_matrix[idx].mean(axis=0).A1.reshape(1, -1)  # Convert np.matrix to ndarray
+        sim = cosine_similarity(avg_vector, tfidf_matrix[idx])
+        most_representative_idx = idx[np.argmax(sim)]
+        avg.append(sentences[most_representative_idx])
+
+    return " ".join(sorted(avg, key=sentences.index))
+
+# 📊 Sentiment Detection
+def analyze_sentiment(text):
+    result = sentiment_analyzer(text)[0]
+    label = result["label"]
+    score = result["score"]
+
+    if "star" in label:
+        stars = int(label[0])
+        if stars <= 2:
+            label = "NEGATIVE"
+        elif stars == 3:
+            label = "NEUTRAL"
+        else:
+            label = "POSITIVE"
+
+    return {
+        "label": label,
+        "score": score
+    }
+
+# 🔥 Emotion Detection (heuristic-based)
+def detect_emotion(text):
+    text_lower = text.lower()
+    if "angry" in text_lower or "hate" in text_lower:
+        return "anger"
+    elif "happy" in text_lower or "love" in text_lower:
+        return "joy"
+    elif "sad" in text_lower or "disappointed" in text_lower:
+        return "sadness"
+    elif "confused" in text_lower or "unclear" in text_lower:
+        return "confusion"
+    else:
+        return "neutral"
+
+# 🧩 Aspect-Based Sentiment (mock)
+def extract_aspect_sentiment(text, aspects: list):
+    results = {}
+    text_lower = text.lower()
+    for asp in aspects:
+        label = "positive" if asp in text_lower and "not" not in text_lower else "neutral"
+        results[asp] = {
+            "label": label,
+            "confidence": 0.85
+        }
+    return results
+
+# ✅ Pydantic Schemas for FastAPI
+class ReviewInput(BaseModel):
+    text: str
+    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
+    industry: str = "Generic"
+    aspects: bool = False
+    follow_up: Optional[str] = None
+    product_category: Optional[str] = None
+    device: Optional[str] = None
+
+class BulkReviewInput(BaseModel):
+    reviews: List[str]
+    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
+    industry: str = "Generic"
+    aspects: bool = False
+    product_category: Optional[str] = None
+    device: Optional[str] = None
+
+class TranslationInput(BaseModel):
+    text: str
+    target_lang: str = "fr"
+
+class ChatInput(BaseModel):
+    question: str
     context: str