Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,122 +1,138 @@
 import os
-os.environ["TRANSFORMERS_CACHE"] = "/cache"  # Hugging Face Spaces cache directory
+import tempfile
+os.environ["TRANSFORMERS_CACHE"] = "/cache"
 
 import streamlit as st
 import fitz  # PyMuPDF
 from transformers import pipeline
 
-# Set page config
+# Security headers and config
 st.set_page_config(
     page_title="PrepPal",
     page_icon="📘",
     layout="wide",
-    menu_items={
-        'About': "PrepPal - AI-powered study assistant"
-    }
+    menu_items={'About': "PrepPal - AI-powered PDF summarizer"}
 )
 
-# Load summarizer model with error handling
+st.markdown("""
+<meta http-equiv="Content-Security-Policy" content="default-src 'self'; script-src 'self' 'unsafe-inline'; style-src 'self' 'unsafe-inline'; img-src 'self' data:;">
+""", unsafe_allow_html=True)
+
 @st.cache_resource
 def load_summarizer():
     try:
         return pipeline(
             "summarization", 
-            model="sshleifer/distilbart-cnn-12-6",
-            device=-1  # Use CPU (more reliable in Spaces)
+            model="facebook/bart-large-cnn",  # Reliable medium-size model
+            device=-1  # Force CPU
         )
     except Exception as e:
-        st.error(f"❌ Failed to load model: {str(e)}")
+        st.error(f"Model loading failed: {str(e)}")
         return None
 
-# Extract text from PDF with size limit
-def extract_text_from_pdf(uploaded_file):
+def extract_text(uploaded_file):
     text = ""
     try:
-        # Check file size (max 5MB)
-        if uploaded_file.size > 5_000_000:
-            st.error("File too large (max 5MB)")
-            return ""
-            
-        with fitz.open(stream=uploaded_file.read(), filetype="pdf") as doc:
-            for page in doc:
-                text += page.get_text()
+        # Save to temp file first (fixes 403 issues)
+        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"❌ Error extracting text: {str(e)}")
+        st.error(f"PDF processing error: {str(e)}")
         return ""
 
-# Summarize text in chunks
-def summarize_text(text, summarizer, max_chunk_length=2000):
-    if not text or not summarizer:
-        return ""
-        
-    try:
-        chunks = [text[i:i+max_chunk_length] for i in range(0, len(text), max_chunk_length)]
-        summary = ""
-        for chunk in chunks:
-            result = summarizer(
-                chunk,
-                max_length=130,
-                min_length=30,
-                do_sample=False
-            )
-            summary += result[0]['summary_text'] + "\n"
-        return summary.strip()
-    except Exception as e:
-        st.error(f"❌ Summarization failed: {str(e)}")
+def summarize(text, model, max_chunk=1500):
+    if not text or not model:
         return ""
-
-# Load model
-summarizer = load_summarizer()
-
-# UI Layout
-st.title("📘 PrepPal - Study Assistant")
-tab1, tab2, tab3 = st.tabs(["📄 Summarize Notes", "❓ Ask a Doubt", "💬 Feedback"])
-
-with tab1:
-    st.header("PDF Summarizer")
-    st.write("Upload your class notes in PDF format to receive a summarized version.")
     
-    uploaded_pdf = st.file_uploader(
-        "Choose a PDF file (max 5MB)",
-        type=["pdf"],
-        accept_multiple_files=False
-    )
-
-    if uploaded_pdf and summarizer:
-        with st.spinner("Extracting text..."):
-            pdf_text = extract_text_from_pdf(uploaded_pdf)
-
-        if pdf_text:
-            st.subheader("Extracted Text Preview")
-            st.text_area("", pdf_text[:1000] + "...", height=200, disabled=True)
+    chunks = [text[i:i+max_chunk] for i in range(0, len(text), max_chunk)]
+    summary = []
+    
+    for chunk in chunks:
+        result = model(
+            chunk,
+            max_length=150,
+            min_length=50,
+            do_sample=False
+        )
+        summary.append(result[0]['summary_text'])
+    
+    return "\n".join(summary)
 
-            if st.button("✂️ Summarize"):
-                with st.spinner("Summarizing... Please wait."):
-                    summary = summarize_text(pdf_text, summarizer)
+# Main App with all 3 tabs
+def main():
+    st.title("📘 PrepPal - Study Assistant")
+    
+    # Create all three tabs
+    tab1, tab2, tab3 = st.tabs(["📄 Summarize Notes", "❓ Ask a Doubt", "💬 Feedback"])
+    
+    with tab1:
+        st.header("PDF Summarizer")
+        st.write("Upload your PDF (max 10MB) for an AI-generated summary")
+        
+        uploaded_file = st.file_uploader(
+            "Choose PDF file",
+            type=["pdf"],
+            accept_multiple_files=False,
+            key="pdf_uploader"
+        )
+        
+        if uploaded_file:
+            if uploaded_file.size > 10_000_000:  # 10MB limit
+                st.error("File too large (max 10MB)")
+            else:
+                with st.spinner("Extracting text..."):
+                    text = extract_text(uploaded_file)
                 
-                if summary:
-                    st.subheader("✅ Summary")
-                    st.text_area("Summary Output", summary, height=300)
-                    st.download_button(
-                        "⬇️ Download Summary",
-                        data=summary,
-                        file_name="summary.txt",
-                        mime="text/plain"
-                    )
-                else:
-                    st.warning("No summary generated")
-
-with tab2:
-    st.header("Ask Questions About Your Notes")
-    st.info("🔧 This feature is coming soon! You'll be able to ask questions about your uploaded notes.")
-
-with tab3:
-    st.header("Help Improve PrepPal")
-    feedback = st.text_area("Your feedback or suggestions")
-    if st.button("Submit Feedback"):
-        st.success("Thank you for your feedback! We'll use it to improve PrepPal.")
+                if text:
+                    with st.expander("View extracted text"):
+                        st.text(text[:1000] + "...")
+                    
+                    if st.button("Generate Summary", key="summarize_btn"):
+                        with st.spinner("Summarizing..."):
+                            model = load_summarizer()
+                            if model:
+                                summary = summarize(text, model)
+                                
+                                st.subheader("AI Summary")
+                                st.write(summary)
+                                
+                                st.download_button(
+                                    "Download Summary",
+                                    data=summary,
+                                    file_name="summary.txt",
+                                    mime="text/plain",
+                                    key="download_btn"
+                                )
+    
+    with tab2:
+        st.header("Ask a Question")
+        st.write("Coming Soon: Ask questions about your uploaded documents")
+        st.image("https://via.placeholder.com/600x200?text=Question+Answering+Feature+Coming+Soon", 
+                caption="AI question answering will be available in the next update")
+        
+        # Placeholder for future functionality
+        question = st.text_input("What would you like to ask about your document?")
+        if question:
+            st.info("This feature is currently in development. Please check back soon!")
+    
+    with tab3:
+        st.header("Your Feedback")
+        st.write("Help us improve PrepPal")
+        
+        feedback = st.text_area("What do you think about PrepPal?")
+        if st.button("Submit Feedback", key="feedback_btn"):
+            if feedback:
+                st.success("Thank you for your feedback! We'll use this to improve the app.")
+                # In a real app, you would store this feedback somewhere
+            else:
+                st.warning("Please enter your feedback before submitting")
 
-# Footer
-st.markdown("---")
-st.caption("PrepPal v1.0 | AI-powered study assistant")
+if __name__ == "__main__":
+    main()