Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,186 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+"""
+AI Notes Summarizer - Main Application
+A Streamlit web application for summarizing PDF files and text content using AI.
+"""
+
 import streamlit as st
+import os
+from pathlib import Path
 
-"""
-# Welcome to Streamlit!
+# Import custom modules
+from modules.pdf_processor import PDFProcessor
+from modules.text_summarizer import TextSummarizer
+from modules.utils import setup_logging, validate_input, display_summary_stats, format_file_size
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# Initialize components
+@st.cache_resource
+def initialize_components():
+    """Initialize PDF processor and text summarizer"""
+    pdf_processor = PDFProcessor()
+    text_summarizer = TextSummarizer()
+    return pdf_processor, text_summarizer
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+def main():
+    """Main application function"""
+    st.set_page_config(
+        page_title="AI Notes Summarizer",
+        page_icon="📝",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+
+    # Initialize components
+    pdf_processor, text_summarizer = initialize_components()
+
+    # App header
+    st.title("📝 AI Notes Summarizer")
+    st.markdown("Transform your lengthy documents and notes into concise, bullet-point summaries using AI.")
+
+    # Sidebar for options
+    st.sidebar.header("⚙️ Settings")
+
+    # Model selection
+    model_options = {
+        "BART (Recommended)": "facebook/bart-large-cnn",
+        "T5 Small": "t5-small",
+        "DistilBART": "sshleifer/distilbart-cnn-12-6"
+    }
+
+    selected_model = st.sidebar.selectbox(
+        "Choose AI Model:",
+        options=list(model_options.keys()),
+        index=0,
+        help="BART is recommended for best quality summaries"
+    )
+
+    # Update text summarizer model if changed
+    if text_summarizer.model_name != model_options[selected_model]:
+        text_summarizer.model_name = model_options[selected_model]
+        text_summarizer.summarizer = None  # Reset to reload model
+
+    # Summary length options
+    summary_length = st.sidebar.select_slider(
+        "Summary Length:",
+        options=["Short", "Medium", "Long"],
+        value="Medium",
+        help="Choose the desired length of the summary"
+    )
+
+    # Update summary length settings
+    length_settings = {
+        "Short": (30, 150),
+        "Medium": (50, 300),
+        "Long": (100, 500)
+    }
+    text_summarizer.min_summary_length, text_summarizer.max_summary_length = length_settings[summary_length]
+
+    # Main content area
+    tab1, tab2 = st.tabs(["📄 PDF Upload", "📝 Text Input"])
+
+    with tab1:
+        st.header("Upload PDF File")
+        st.markdown("Upload a PDF file to extract and summarize its content.")
+
+        uploaded_file = st.file_uploader(
+            "Choose a PDF file",
+            type=['pdf'],
+            help="Upload a PDF file (max 10MB)"
+        )
+
+        if uploaded_file is not None:
+            # Display file info
+            file_size = format_file_size(uploaded_file.size)
+            st.info(f"📄 **File:** {uploaded_file.name} ({file_size})")
+
+            # Process PDF button
+            if st.button("📖 Extract & Summarize PDF", type="primary"):
+                with st.spinner("Processing PDF file..."):
+                    # Extract text from PDF
+                    extracted_text = pdf_processor.process_pdf(uploaded_file)
+
+                    if extracted_text:
+                        st.success("✅ Text extracted successfully!")
+
+                        # Show extracted text preview
+                        with st.expander("📝 View Extracted Text (Preview)"):
+                            st.text_area(
+                                "Extracted Content:",
+                                value=extracted_text[:1000] + "..." if len(extracted_text) > 1000 else extracted_text,
+                                height=200,
+                                disabled=True
+                            )
+
+                        # Generate summary
+                        summary = text_summarizer.summarize_text(extracted_text)
+
+                        if summary:
+                            st.success("✅ Summary generated successfully!")
+
+                            # Display summary
+                            st.subheader("📋 Summary")
+                            st.markdown(summary)
+
+                            # Display statistics
+                            st.subheader("📊 Statistics")
+                            display_summary_stats(extracted_text, summary)
+
+                            # Download option
+                            st.download_button(
+                                label="💾 Download Summary",
+                                data=summary,
+                                file_name=f"{uploaded_file.name}_summary.txt",
+                                mime="text/plain"
+                            )
+
+    with tab2:
+        st.header("Direct Text Input")
+        st.markdown("Paste your text content directly for summarization.")
+
+        text_input = st.text_area(
+            "Enter your text here:",
+            height=300,
+            placeholder="Paste your text content here...",
+            help="Minimum 100 characters required for effective summarization"
+        )
+
+        # Character count
+        char_count = len(text_input)
+        st.caption(f"Characters: {char_count:,}")
+
+        if st.button("🚀 Summarize Text", type="primary"):
+            if validate_input(text_input, min_length=100):
+                # Generate summary
+                summary = text_summarizer.summarize_text(text_input)
+
+                if summary:
+                    st.success("✅ Summary generated successfully!")
+
+                    # Display summary
+                    st.subheader("📋 Summary")
+                    st.markdown(summary)
+
+                    # Display statistics
+                    st.subheader("📊 Statistics")
+                    display_summary_stats(text_input, summary)
+
+                    # Download option
+                    st.download_button(
+                        label="💾 Download Summary",
+                        data=summary,
+                        file_name="text_summary.txt",
+                        mime="text/plain"
+                    )
+
+    # Footer
+    st.markdown("---")
+    st.markdown(
+        """
+        <div style='text-align: center; color: #666;'>
+            <p>AI Notes Summarizer | Powered by Hugging Face Transformers</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()