Update app.py

app.py

@@ -1,71 +1,110 @@
 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 keybert import KeyBERT
+from openai import AzureOpenAI  # new
 
-# ─── Streamlit Page Config ─────────────────────────────────────────────────
-st.set_page_config(page_title="Review Analyzer", layout="wide")
+# ─── Azure OpenAI Client ─────────────────────────────────────────────────────
+openai_client = AzureOpenAI(
+    api_key=os.environ["AZURE_OPENAI_KEY"],
+    api_version="2023-05-15",
+    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"]
+)
 
-# ─── Authenticate to Hugging Face Hub ───────────────────────────────────────
-# ensure HF_TOKEN is set in your environment
-token = os.environ.get("HF_TOKEN", "")
-login(token=token, add_to_git_credential=False)
-
-# ─── Cached resource: Sentiment Analysis Pipeline ────────────────────────────
+# ─── (your existing cache decorators) ────────────────────────────────────────
 @st.cache_resource
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
-    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=True)
+    tok = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
+    mdl = AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=True)
     return pipeline(
         "sentiment-analysis",
-        model=model,
-        tokenizer=tokenizer,
+        model=mdl,
+        tokenizer=tok,
         return_all_scores=True
     )
 
-# ─── Cached resource: KeyBERT Model ─────────────────────────────────────────
 @st.cache_resource
 def load_keybert_model():
     return KeyBERT(model="all-MiniLM-L6-v2")
 
-# ─── Main Application ───────────────────────────────────────────────────────
+
 def main():
+    st.title("📊 Review Sentiment & Keyword Analyzer + GPT Insights")
+
+    # ─── Inputs & Models ──────────────────────────────────────────────────────
+    review = st.text_area("Enter your review:")
+    if not st.button("Analyze Review"):
+        return
+
+    if not review:
+        st.warning("Please enter a review to analyze.")
+        return
+
+    # ─── Sentiment & Keywords ─────────────────────────────────────────────────
     sentiment_pipeline = load_sentiment_pipeline()
     kw_model = load_keybert_model()
 
-    st.title("📊 Review Sentiment & Keyword Analyzer")
-    review = st.text_area("Enter your review:")
+    scores = sentiment_pipeline(review)[0]
+    sentiment_results = {item['label']: float(item['score']) for item in scores}
+    st.subheader("Sentiment Scores")
+    st.json({k: round(v, 4) for k, v in sentiment_results.items()})
+
+    keywords = kw_model.extract_keywords(
+        review,
+        keyphrase_ngram_range=(1, 2),
+        stop_words="english",
+        top_n=3
+    )
+    st.subheader("Top 3 Keywords")
+    for kw, score in keywords:
+        st.write(f"- **{kw}** (Score: {score:.4f})")
+
+    # ─── Determine Highest Sentiment ───────────────────────────────────────────
+    max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
+    st.subheader("Highest Sentiment")
+    st.write(f"{max_label} (Score: {max_score:.4f})")
+
+    # ─── GPT-Driven Analysis & Suggestions ────────────────────────────────────
+    st.subheader("GPT Analysis & Seller Suggestions")
+
+    # build a single text prompt for GPT
+    prompt = f"""
+You are a helpful assistant for e-commerce sellers. 
+Here is a product review, its sentiment breakdown, and the top keywords:
+
+Review:
+\"\"\"{review}\"\"\"
+
+Sentiment scores:
+{sentiment_results}
+
+Top keywords:
+{[kw for kw, _ in keywords]}
+
+First, provide a one-paragraph professional analysis of what the customer feels and why (combine sentiment + keywords). 
+Then, give 3 detailed, actionable suggestions the seller can implement to improve future reviews or address the feedback.
+"""
+
+    # call Azure OpenAI
+    response = openai_client.chat.completions.create(
+        engine="gpt-35-turbo",                  # or your deployed model name
+        messages=[
+            {"role": "system", "content": "You are a product-feedback analyst."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.7,
+        max_tokens=400
+    )
+
+    # display GPT’s reply
+    gpt_reply = response.choices[0].message.content
+    st.markdown(gpt_reply)
 
-    if st.button("Analyze Review"):
-        if not review:
-            st.warning("Please enter a review to analyze.")
-            return
-
-        # Sentiment Analysis
-        scores = sentiment_pipeline(review)[0]
-        sentiment_results = {item['label']: float(item['score']) for item in scores}
-        st.subheader("Sentiment Scores")
-        st.json({k: round(v, 4) for k, v in sentiment_results.items()})
-
-        # Keyword Extraction (top 3)
-        keywords = kw_model.extract_keywords(
-            review,
-            keyphrase_ngram_range=(1, 2),
-            stop_words="english",
-            top_n=3
-        )
-        st.subheader("Top 3 Keywords")
-        for kw, score in keywords:
-            st.write(f"- **{kw}** (Score: {score:.4f})")
-
-        # Highest Sentiment Label
-        max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
-        st.subheader("Highest Sentiment")
-        st.write(f"{max_label} (Score: {max_score:.4f})")
-        st.write("Keywords used for this analysis:")
-        st.write(', '.join([kw for kw, _ in keywords]))
 
 if __name__ == "__main__":
-    main()
+    # make sure your env vars are set: AZURE_OPENAI_KEY, AZURE_OPENAI_ENDPOINT
+    main()