Update app.py

app.py

@@ -1,71 +1,82 @@
 # app.py
 import streamlit as st
-from transformers import pipeline
-from PIL import Image
-import io
-from scipy.io.wavfile import write as write_wav
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
 
-def generate_image_caption(image):
-    """Generates a caption for the given image"""
-    img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    result = img2caption(image)
-    return result[0]['generated_text']
+# Page config
+st.set_page_config(
+    page_title="Amazon Review Sentiment Analysis",
+    page_icon="📊",
+    layout="wide"
+)
 
-def text2story(text):
-    """Generates a children's story from text input"""
-    story_prompt = f"Create a funny 100-word story for 8-year-olds about: {text}. Include: 1) A silly character 2) Magical object 3) Sound effects 4) Happy ending"
-    
-    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
-    story_text = pipe(
-        story_prompt,
-        max_new_tokens=200,
-        temperature=0.9,
-        top_k=50
-    )[0]['generated_text']
-    return story_text.split("Happy ending")[-1].strip()
+@st.cache_resource
+def load_model():
+    """Load the model and tokenizer."""
+    model_name = "LiYuan/amazon-review-sentiment-analysis"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return tokenizer, model
 
-def story_to_speech(story_text):
-    """Converts story text to audio using TTS"""
-    tts_pipe = pipeline("text-to-speech", model="suno/bark-small")
-    audio_output = tts_pipe(story_text[:400])
-    
-    # Convert to bytes using numpy directly
-    audio_bytes = io.BytesIO()
-    audio_np = (audio_output["audio"] * 32767).astype(np.int16)
-    write_wav(audio_bytes, audio_output["sampling_rate"], audio_np)
-    audio_bytes.seek(0)
-    
-    return audio_bytes
+def predict_sentiment(text, tokenizer, model):
+    """Predict sentiment for given text."""
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return predictions.detach().numpy()[0]
 
 def main():
-    st.title("📖 Image Story Generator with Audio")
-    st.write("Upload an image to get a magical story read aloud!")
+    st.title("📊 Amazon Review Sentiment Analysis")
+    st.write("""
+    This application analyzes the sentiment of product reviews and predicts ratings (1-5 stars).
+    Enter your review text below to get started!
+    """)
 
-    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    # Load model
+    with st.spinner("Loading model..."):
+        tokenizer, model = load_model()
 
-    if uploaded_image:
-        image = Image.open(uploaded_image).convert("RGB")
-        st.image(image, use_column_width=True)
-        
-        with st.spinner("✨ Analyzing image..."):
-            caption = generate_image_caption(image)
-        
-        st.subheader("Image Understanding")
-        st.write(caption)
-        
-        with st.spinner("📖 Writing story..."):
-            story = text2story(caption)
-        
-        st.subheader("Magical Story")
-        st.write(story)
+    # Text input
+    text_input = st.text_area("Enter your review text:", height=150)
+
+    if st.button("Analyze Sentiment"):
+        if text_input.strip():
+            with st.spinner("Analyzing..."):
+                # Get prediction
+                predictions = predict_sentiment(text_input, tokenizer, model)
+                predicted_rating = np.argmax(predictions) + 1  # Add 1 since ratings are 1-5
+
+                # Display results
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    st.subheader("Predicted Rating")
+                    st.markdown(f"<h1 style='text-align: center; color: #1f77b4;'>{'⭐' * predicted_rating}</h1>", unsafe_allow_html=True)
+                
+                with col2:
+                    st.subheader("Confidence Scores")
+                    for i, score in enumerate(predictions, 1):
+                        st.progress(float(score))
+                        st.write(f"{i} Stars: {score:.2%}")
+        else:
+            st.warning("Please enter some text to analyze.")
+
+    # Additional information
+    with st.expander("About this Model"):
+        st.write("""
+        This application uses the LiYuan/amazon-review-sentiment-analysis model from HuggingFace.
+        The model is based on DistilBERT and was trained on a large dataset of Amazon product reviews.
+        It can predict ratings from 1 to 5 stars based on the review text.
         
-        if st.button("🎧 Read Story Aloud"):
-            with st.spinner("🔊 Generating audio..."):
-                try:
-                    audio_bytes = story_to_speech(story)
-                    st.audio(audio_bytes, format="audio/wav")
-                except Exception as e:
-                    st.error(f"Error generating audio: {str(e)}")
+        Supported languages:
+        - English
+        - Dutch
+        - German
+        - French
+        - Spanish
+        - Italian
+        """)
 
 if __name__ == "__main__":
-    main()
+    main()