Update app.py

app.py

@@ -2,18 +2,10 @@ import os
 import numpy as np
 import pandas as pd
 import streamlit as st
-from huggingface_hub import login
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 from keybert import KeyBERT
-from openai import AzureOpenAI  # new
-
-# ─── Azure OpenAI Client ─────────────────────────────────────────────────────
-openai_client = AzureOpenAI(
-  api_key = "fbca46bfd8814334be46a2e5c323904c", # use your key here
-  api_version = "2023-05-15", # apparently HKUST uses a deprecated version
-  azure_endpoint = "https://hkust.azure-api.net" # per HKUST instructions
-)
 
+# ─── Sentiment & Keyword Models ─────────────────────────────────────────────
 @st.cache_resource
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
@@ -30,6 +22,16 @@ def load_sentiment_pipeline():
 def load_keybert_model():
     return KeyBERT(model="all-MiniLM-L6-v2")
 
+# ─── FLAN-T5 Generation Pipeline ────────────────────────────────────────────
+@st.cache_resource
+def load_flant5_pipeline():
+    # High-level helper for text2text generation
+    return pipeline(
+        "text2text-generation",
+        model="google/flan-t5-base",
+        tokenizer="google/flan-t5-base"
+    )
+
 LABEL_MAP = {
     "LABEL_0": "Very Negative",
     "LABEL_1": "Negative",
@@ -49,23 +51,22 @@ def main():
         st.warning("Please enter a review to analyze.")
         return
 
-    # Initialize progress bar
     progress = st.progress(0)
 
     # Load models
     progress.text("Loading models...")
     sentiment_pipeline = load_sentiment_pipeline()
     kw_model = load_keybert_model()
+    generation_pipeline = load_flant5_pipeline()
     progress.progress(20)
 
-    # Run sentiment analysis
+    # Sentiment
     progress.text("Analyzing sentiment...")
     raw_scores = sentiment_pipeline(review)[0]
-    # Map labels
     sentiment_results = {LABEL_MAP[item['label']]: float(item['score']) for item in raw_scores}
     progress.progress(40)
 
-    # Extract keywords
+    # Keywords
     progress.text("Extracting keywords...")
     keywords = kw_model.extract_keywords(
         review,
@@ -75,7 +76,7 @@ def main():
     )
     progress.progress(60)
 
-    # Display scores and keywords side by side
+    # Display
     col1, col2 = st.columns(2)
     with col1:
         st.subheader("Sentiment Scores")
@@ -85,7 +86,7 @@ def main():
         for kw, score in keywords:
             st.write(f"• {kw} ({score:.4f})")
 
-    # Bar chart
+    # Chart
     progress.text("Rendering chart...")
     df_scores = pd.DataFrame.from_dict(sentiment_results, orient='index', columns=['score'])
     df_scores.index.name = 'Sentiment'
@@ -96,33 +97,25 @@ def main():
     max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
     st.markdown(f"**Highest Sentiment:** **{max_label}** ({max_score:.4f})")
 
-    # GPT-Driven Analysis & Suggestions
+    # FLAN-T5 Analysis & Suggestions
     progress.text("Generating insights...")
     prompt = f"""
 You are an analytical amazon feedback expert.
 Review: \"{review}\"
 Sentiment Scores: {sentiment_results}
 Top Keywords: {[kw for kw, _ in keywords]}
+
 Tasks:
 1. Analysis: Write a concise paragraph (3 sentences) interpreting customer sentiment by combining the scores and keywords.
 2. Recommendations: Three separate paragraphs with actionable suggestions (max 30 words each).
 """
+    output = generation_pipeline(prompt, max_length=200, do_sample=False)[0]['generated_text']
+    st.markdown(output)
 
-    response = openai_client.chat.completions.create(
-        model="gpt-35-turbo",
-        messages=[
-            {"role": "system", "content": "You are a product-feedback analyst."},
-            {"role": "user", "content": prompt}
-        ],
-        temperature=0.7,
-        max_tokens=200
-    )
-    gpt_reply = response.choices[0].message.content.strip()
-    st.markdown(gpt_reply)
-
-    # Complete
     progress.progress(100)
     progress.text("Done!")
 
+
 if __name__ == "__main__":
     main()
+