Update app.py

app.py

@@ -1,154 +1,72 @@
-# FIRST import and FIRST Streamlit command
+import os
 import streamlit as st
-st.set_page_config(
-    page_title="Magic Story Generator",
-    layout="centered",
-    page_icon="📖"
-)
-
-# Other imports
-import re
-import time
-import torch
-import tempfile
-from PIL import Image
-from gtts import gTTS
-from transformers import pipeline, AutoTokenizer
-
-# --- Constants & Setup ---
-st.title("📖✨ Turn Images into Children's Stories")
-
-# --- Model Loading (Cached) ---
-@st.cache_resource(show_spinner=False)
-def load_models():
-    # Image captioning model
-    captioner = pipeline(
-        "image-to-text",
-        model="Salesforce/blip-image-captioning-base",
-        device=0 if torch.cuda.is_available() else -1
+from huggingface_hub import login
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from keybert import KeyBERT
+
+# ─── Cached resource: Sentiment Analysis Pipeline ────────────────────────────
+@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
     )
-    
-    # Optimized story generation model
-    tokenizer = AutoTokenizer.from_pretrained("Deepthoughtworks/gpt-neo-2.7B__low-cpu")
-    storyteller = pipeline(
-        "text-generation",
-        model="Deepthoughtworks/gpt-neo-2.7B__low-cpu",
+    return pipeline(
+        "sentiment-analysis",
+        model=model,
         tokenizer=tokenizer,
-        device_map="auto",
-        torch_dtype=torch.float32,  # Changed to float32 for better CPU compatibility
-        max_new_tokens=150,         # Reduced length for faster generation
-        temperature=0.85,
-        top_k=40,
-        top_p=0.92,
-        repetition_penalty=1.15,
-        pad_token_id=tokenizer.eos_token_id  # Added for padding control
+        return_all_scores=True
     )
-    
-    return captioner, storyteller
-
-caption_pipe, story_pipe = load_models()
-
-# --- Main Application Flow ---
-uploaded_image = st.file_uploader(
-    "Upload a children's book style image:",
-    type=["jpg", "jpeg", "png"]
-)
-
-if uploaded_image:
-    # Process image
-    image = Image.open(uploaded_image).convert("RGB")
-    st.image(image, use_container_width=True)  # Fixed deprecated parameter
-
-    # Generate caption
-    with st.spinner("🔍 Analyzing image..."):
-        try:
-            caption_result = caption_pipe(image)
-            image_caption = caption_result[0].get("generated_text", "").strip()
-        except Exception as e:
-            st.error(f"❌ Image analysis failed: {str(e)}")
-            st.stop()
-    
-    if not image_caption:
-        st.error("❌ Couldn't understand this image. Please try another!")
-        st.stop()
-    
-    st.success(f"**Image Understanding:** {image_caption}")
-
-    # Create story prompt
-    story_prompt = f"""Write a 50 to 100 words children's story based on: {image_caption}
-Requirements:
-- Exclude your thinking process
-Story:"""
-
-    # Generate story with progress
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    
-    try:
-        with st.spinner("📝 Crafting magical story..."):
-            start_time = time.time()
-            
-            def update_progress(step):
-                progress = min(step/5, 1.0)
-                progress_bar.progress(progress)
-                status_text.text(f"Step {int(step)}/5: {'📖'*int(step)}")
-            
-            update_progress(1)
-            story_result = story_pipe(
-                story_prompt,
-                do_sample=True,
-                num_return_sequences=1
-            )
-            
-            update_progress(4)
-            generation_time = time.time() - start_time
-            st.info(f"Story generated in {generation_time:.1f} seconds")
-
-            # Process output
-            raw_story = story_result[0]['generated_text']
-            clean_story = raw_story.split("Story:")[-1].strip()
-            clean_story = re.sub(r'\n+', '\n\n', clean_story)  # Improve paragraph spacing
-            
-            # Format story text
-            final_story = ""
-            for paragraph in clean_story.split('\n\n'):
-                paragraph = paragraph.strip()
-                if paragraph:
-                    sentences = []
-                    for sent in re.split(r'(?<=[.!?]) +', paragraph):
-                        sent = sent.strip()
-                        if sent:
-                            if len(sent) > 1 and not sent.endswith(('.','!','?')):
-                                sent += '.'
-                            sentences.append(sent[0].upper() + sent[1:])
-                    final_story += ' '.join(sentences) + '\n\n'
-
-            update_progress(5)
-            time.sleep(0.5)
-
-    except Exception as e:
-        st.error(f"❌ Story generation failed: {str(e)}")
-        st.stop()
-
-    finally:
-        progress_bar.empty()
-        status_text.empty()
-
-    # Display story
-    st.subheader("✨ Your Magical Story")
-    st.write(final_story.strip())
-
-    # Audio conversion
-    with st.spinner("🔊 Creating audio version..."):
-        try:
-            audio = gTTS(text=final_story, lang="en", slow=False)
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
-                audio.save(tmp_file.name)
-                st.audio(tmp_file.name, format="audio/mp3")
-        except Exception as e:
-            st.error(f"❌ Audio conversion failed: {str(e)}")
 
-# Footer
-st.markdown("---")
-st.markdown("📚 Made with ♥ by The Story Wizard • [Report Issues](https://example.com)")
+# ─── Cached resource: KeyBERT Model ─────────────────────────────────────────
+@st.cache_resource
+def load_keybert_model():
+    return KeyBERT(model="all-MiniLM-L6-v2")
+
+# ─── Main Application ───────────────────────────────────────────────────────
+def main():
+    sentiment_pipeline = load_sentiment_pipeline()
+    kw_model = load_keybert_model()
+
+    st.set_page_config(page_title="Review Analyzer", layout="wide")
+    st.title("📊 Review Sentiment & Keyword Analyzer")
+
+    review = st.text_area("Enter your review:")
+
+    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']: round(item['score'], 4) for item in scores}
+        st.subheader("Sentiment Scores")
+        st.json(sentiment_results)
+
+        # Keyword Extraction
+        keywords = kw_model.extract_keywords(
+            review,
+            keyphrase_ngram_range=(1, 2),
+            stop_words="english",
+            top_n=5
+        )
+        st.subheader("Top Keywords")
+        for kw, score in keywords:
+            st.write(f"- **{kw}** (Score: {score:.4f})")
+
+        # Composite Score
+        avg_sentiment = sum(sentiment_results.values()) / len(sentiment_results)
+        st.subheader("Composite Score with Keywords")
+        st.write(f"Average Sentiment Score: {avg_sentiment:.4f}")
+        st.write("Keywords used for this score:")
+        st.write(', '.join([kw for kw, _ in keywords]))
+
+if __name__ == "__main__":
+    main()