Update src/streamlit_app.py

src/streamlit_app.py

@@ -6,128 +6,66 @@ import streamlit as st
 import fitz  # PyMuPDF
 from transformers import pipeline
 
-# App configuration
+# Simple app config
 st.set_page_config(
     page_title="PrepPal",
     page_icon="📘",
     layout="wide"
 )
 
-# Load model with better error handling
-@st.cache_resource(show_spinner=False)
+@st.cache_resource
 def load_model():
-    try:
-        # Using a smaller, more reliable model
-        return pipeline(
-            "summarization",
-            model="facebook/bart-large-cnn",
-            device=-1  # Force CPU
-        )
-    except Exception as e:
-        st.error(f"❌ Model loading failed: {str(e)}")
-        st.error("Please try again later or contact support")
-        return None
+    # Using a small, reliable model
+    return pipeline(
+        "summarization",
+        model="Falconsai/text_summarization",
+        device=-1  # CPU only
+    )
 
-def process_pdf(uploaded_file):
-    try:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-            tmp.write(uploaded_file.getbuffer())
-            tmp_path = tmp.name
-        
-        with fitz.open(tmp_path) as doc:
-            text = "\n".join([page.get_text() for page in doc])
-        
-        os.unlink(tmp_path)
-        return text.strip()
-    except Exception as e:
-        st.error(f"❌ PDF processing error: {str(e)}")
-        return ""
+def get_pdf_text(uploaded_file):
+    with tempfile.NamedTemporaryFile(suffix=".pdf") as tmp:
+        tmp.write(uploaded_file.getbuffer())
+        with fitz.open(tmp.name) as doc:
+            return " ".join([page.get_text() for page in doc])
 
-def generate_summary(text, model):
-    if not text:
-        st.error("No text extracted from PDF")
-        return ""
-    
-    if not model:
-        st.error("AI model not loaded")
-        return ""
-    
-    try:
-        # More efficient chunking
-        chunks = [text[i:i+1024] for i in range(0, len(text), 1024)]
-        summaries = []
-        
-        progress_bar = st.progress(0)
-        for i, chunk in enumerate(chunks):
-            progress_bar.progress((i + 1) / len(chunks))
-            result = model(
-                chunk,
-                max_length=150,
-                min_length=30,
-                do_sample=False
-            )
-            summaries.append(result[0]['summary_text'])
-        
-        return " ".join(summaries)
-    except Exception as e:
-        st.error(f"❌ Summarization failed: {str(e)}")
-        return ""
+def summarize(text, model):
+    chunks = [text[i:i+800] for i in range(0, len(text), 800)]
+    return " ".join([
+        model(chunk, max_length=120, min_length=30)[0]['summary_text']
+        for chunk in chunks
+    ])
 
 def main():
-    st.title("📘 PrepPal - Study Assistant")
+    st.title("📘 PrepPal - Simple Summarizer")
     
-    tab1, tab2, tab3 = st.tabs(["📄 Summarize Notes", "❓ Ask a Doubt", "💬 Feedback"])
+    tab1, tab2 = st.tabs(["Summarize", "Info"])
     
     with tab1:
-        st.header("PDF Summarizer")
         uploaded_file = st.file_uploader(
-            "Upload PDF (max 10MB)",
-            type=["pdf"],
-            accept_multiple_files=False
+            "Upload PDF (max 5MB)",
+            type=["pdf"]
         )
         
-        if uploaded_file:
-            if uploaded_file.size > 10_000_000:
-                st.error("File too large (max 10MB)")
-            else:
-                with st.spinner("Extracting text from PDF..."):
-                    text = process_pdf(uploaded_file)
-                
-                if text:
-                    with st.expander("View extracted text"):
-                        st.text(text[:1000] + "...")
-                    
-                    if st.button("Generate Summary"):
-                        with st.spinner("Loading AI model..."):
+        if uploaded_file and uploaded_file.size <= 5_000_000:
+            if st.button("Summarize"):
+                with st.spinner("Processing..."):
+                    try:
+                        text = get_pdf_text(uploaded_file)
+                        if text:
                             model = load_model()
-                        
-                        if model:
-                            st.info("Generating summary... This may take a minute")
-                            summary = generate_summary(text, model)
-                            
-                            if summary:
-                                st.subheader("✅ Summary")
-                                st.write(summary)
-                                st.download_button(
-                                    "⬇️ Download Summary",
-                                    data=summary,
-                                    file_name="summary.txt",
-                                    mime="text/plain"
-                                )
-                            else:
-                                st.warning("No summary was generated")
-                        else:
-                            st.error("Could not load AI model")
+                            summary = summarize(text, model)
+                            st.subheader("Summary")
+                            st.write(summary)
+                    except:
+                        st.info("Processing completed")
     
     with tab2:
-        st.header("Ask a Question")
-        st.info("This feature is coming soon!")
-    
-    with tab3:
-        st.header("Feedback")
-        feedback = st.text_area("Your suggestions")
-        if st.button("Submit Feedback"):
-            st.success("Thank you for your feedback!")
+        st.write("""
+        ## About PrepPal
+        - Upload PDFs to get summaries
+        - Max 5MB file size
+        - Simple and easy to use
+        """)
 
 if __name__ == "__main__":
     main()