Update app.py

app.py

@@ -2,6 +2,9 @@ import streamlit as st
 from transformers import pipeline
 import pandas as pd
 from datetime import datetime
+from PIL import Image
+import requests
+from io import BytesIO
 
 # Constants
 RATING_MAP = {
@@ -10,6 +13,13 @@ RATING_MAP = {
     2: "Positive (⭐⭐⭐)"
 }
 
+# Emoji mapping based on ratings
+EMOJI_MAP = {
+    0: "😠",  # Angry face for negative
+    1: "😐",  # Neutral face
+    2: "😊"   # Happy face
+}
+
 @st.cache_resource
 def load_models():
     # Load sentiment analysis model
@@ -17,48 +27,47 @@ def load_models():
         "text-classification", 
         model="AndrewLi403/CustomModel_tripadvisor_finetuned"
     )
-    # Load fake review detection model (automatically handles sigmoid)
-    fake_detector = pipeline(
-        "text-classification",
-        model="filippoferrari/finetuning-fake-reviews-detector-model"
-    )
-    return sentiment_model, fake_detector
+    # Load text-to-emoji model
+    emoji_pipe = pipeline("text-classification", model="j-hartmann/emotion-english-roberta-large")
+    return sentiment_model, emoji_pipe
 
-def analyze_review(text, sentiment_model, fake_detector):
+def analyze_review(text, sentiment_model, emoji_pipe):
     # Sentiment analysis
     sentiment_result = sentiment_model(text)[0]
     rating = int(sentiment_result['label'].split('_')[-1])
     
-    # Fake detection
-    fake_result = fake_detector(text)[0]
-    is_fake = fake_result['label'] == 'FAKE'
+    # Emoji analysis
+    emoji_result = emoji_pipe(text)[0]
+    dominant_emoji = emoji_result['label']
     
     return {
         'sentiment': RATING_MAP[rating],
         'sentiment_score': sentiment_result['score'],
-        'is_fake': is_fake,
-        'fake_score': fake_result['score']
+        'rating': rating,
+        'dominant_emoji': dominant_emoji,
+        'emoji_confidence': emoji_result['score']
     }
 
 def main():
     st.title("Hotel Review Analyzer")
-    st.markdown("Analyze sentiment and detect fake reviews")
+    st.markdown("Analyze sentiment and detect emotional tone")
     
     # Load models
-    sentiment_model, fake_detector = load_models()
+    sentiment_model, emoji_pipe = load_models()
     
     # Input
     review_text = st.text_area("Paste your hotel review here:", height=150)
     
     if st.button("Analyze"):
         if review_text:
-            with st.spinner("Analyzing..."):
+            with st.spinner("Analyzing emotions..."):
                 # Get analysis results
-                results = analyze_review(review_text, sentiment_model, fake_detector)
+                results = analyze_review(review_text, sentiment_model, emoji_pipe)
                 
                 # Display results
                 st.subheader("Analysis Results")
                 
+                # First row: Rating and Emoji
                 col1, col2 = st.columns(2)
                 with col1:
                     st.metric("Sentiment Rating", 
@@ -66,18 +75,28 @@ def main():
                             delta=f"{results['sentiment_score']:.2f}")
                 
                 with col2:
-                    st.metric("Authenticity",
-                            "SUSPICIOUS" if results['is_fake'] else "GENUINE",
-                            delta=f"{results['fake_score']:.2f}",
-                            delta_color="inverse" if results['is_fake'] else "normal")
+                    # Display both system emoji and detected emoji
+                    st.metric("Emotional Tone",
+                            f"{EMOJI_MAP[results['rating']]} (System) / {results['dominant_emoji']} (Detected)",
+                            delta=f"Confidence: {results['emoji_confidence']:.2f}")
                 
-                # Warning for fake reviews
-                if results['is_fake']:
-                    st.warning("⚠️ This review shows characteristics of potentially fake content!")
+                # Visual emoji display
+                st.subheader("Emotional Response")
+                cols = st.columns(3)
+                with cols[1]:
+                    st.header(EMOJI_MAP[results['rating']] * 5)  # Repeat emoji for visual impact
+                    st.caption("Based on your star rating")
+                
+                # Emotion breakdown
+                with st.expander("Detailed Emotion Analysis"):
+                    full_emoji_results = emoji_pipe(review_text, top_k=5)
+                    for emotion in full_emoji_results:
+                        st.progress(
+                            int(emotion['score'] * 100),
+                            text=f"{emotion['label']}: {emotion['score']:.2f}"
+                        )
         else:
             st.error("Please enter a review to analyze")
 
 if __name__ == "__main__":
-    main()
-
-    
+    main()