Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 from google.cloud import vision
 import os
-from PIL import Image, ImageDraw
+from PIL import Image, ImageDraw, ImageFont
 import io
 import numpy as np
 from streamlit_option_menu import option_menu
@@ -141,7 +141,7 @@ def display_results(annotated_img, labels, objects, text):
     
     with col1:
         st.markdown('<div class="subheader">Analyzed Image</div>', unsafe_allow_html=True)
-        st.image(annotated_img, use_column_width=True)
+        st.image(annotated_img, use_container_width=True)
     
     with col2:
         st.markdown('<div class="subheader">Analysis Results</div>', unsafe_allow_html=True)
@@ -168,6 +168,94 @@ def display_results(annotated_img, labels, objects, text):
             st.markdown('<div class="result-container">', unsafe_allow_html=True)
             st.markdown(f'<div class="text-item">{text}</div>', unsafe_allow_html=True)
             st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Add Download Summary Image button
+    summary_img = create_summary_image(annotated_img, labels, objects, text)
+    buf = io.BytesIO()
+    summary_img.save(buf, format="JPEG", quality=90)
+    byte_im = buf.getvalue()
+    
+    st.download_button(
+        label="📥 Download Complete Results Summary",
+        data=byte_im,
+        file_name="analysis_summary.jpg",
+        mime="image/jpeg",
+        help="Download a complete image showing the analyzed image and all detected features"
+    )
+
+def create_summary_image(annotated_img, labels, objects, text):
+    """Create a downloadable summary image with analysis results"""
+    # Create a new image with space for results
+    img_width, img_height = annotated_img.size
+    # Make room for text results (adjust height based on content)
+    result_height = 400  # Space for results
+    summary_img = Image.new('RGB', (img_width, img_height + result_height), color=(255, 255, 255))
+    
+    # Paste the annotated image at the top
+    summary_img.paste(annotated_img, (0, 0))
+    
+    # Create a drawing object
+    draw = ImageDraw.Draw(summary_img)
+    
+    # Try to get a font - use default if not available
+    try:
+        font = ImageFont.truetype("arial.ttf", 16)
+        title_font = ImageFont.truetype("arial.ttf", 20)
+    except IOError:
+        font = ImageFont.load_default()
+        title_font = ImageFont.load_default()
+    
+    # Draw title
+    draw.text((20, img_height + 20), "Cosmick Cloud AI Analyzer Results", fill=(65, 105, 225), font=title_font)
+    
+    # Draw divider line
+    draw.line([(0, img_height + 50), (img_width, img_height + 50)], fill=(200, 200, 200), width=2)
+    
+    # Current Y position for drawing text
+    y_pos = img_height + 60
+    
+    # Draw labels
+    if labels:
+        draw.text((20, y_pos), "🏷️ Labels Detected:", fill=(0, 0, 0), font=title_font)
+        y_pos += 30
+        
+        for i, (label, confidence) in enumerate(sorted(labels.items(), key=lambda x: x[1], reverse=True)):
+            if i < 8:  # Limit to top 8 labels to avoid overcrowding
+                draw.text((40, y_pos), f"{label}: {confidence}%", fill=(0, 100, 0), font=font)
+                y_pos += 25
+    
+    # Draw a column divider
+    mid_point = img_width // 2
+    draw.line([(mid_point - 20, img_height + 60), (mid_point - 20, img_height + result_height - 20)], 
+              fill=(200, 200, 200), width=1)
+    
+    # Reset Y position for second column
+    y_pos = img_height + 60
+    
+    # Draw objects in second column
+    if objects:
+        draw.text((mid_point, y_pos), "📦 Objects Detected:", fill=(0, 0, 0), font=title_font)
+        y_pos += 30
+        
+        for i, (obj, confidence) in enumerate(sorted(objects.items(), key=lambda x: x[1], reverse=True)):
+            if i < 8:  # Limit to top 8 objects
+                draw.text((mid_point + 20, y_pos), f"{obj}: {confidence}%", fill=(0, 0, 128), font=font)
+                y_pos += 25
+    
+    # Add text detection summary at the bottom
+    if text:
+        bottom_y = img_height + result_height - 80
+        draw.text((20, bottom_y), "📝 Text Detected:", fill=(0, 0, 0), font=title_font)
+        # Truncate text if too long
+        display_text = text if len(text) < 100 else text[:97] + "..."
+        draw.text((20, bottom_y + 30), display_text, fill=(128, 0, 0), font=font)
+    
+    # Add timestamp
+    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
+    draw.text((img_width - 200, img_height + result_height - 30), 
+              f"Generated: {timestamp}", fill=(100, 100, 100), font=font)
+    
+    return summary_img
 
 class VideoProcessor(VideoProcessorBase):
     """Process video frames for real-time analysis"""
@@ -802,7 +890,7 @@ def main():
                 
                 # Show original image
                 st.markdown('<div class="subheader">Original Image</div>', unsafe_allow_html=True)
-                st.image(image, use_column_width=True)
+                st.image(image, use_container_width=True)
                 
                 # Add analyze button
                 if st.button("Analyze Image"):
@@ -846,7 +934,7 @@ def main():
                 for i, uploaded_file in enumerate(uploaded_files):
                     with cols[i]:
                         image = Image.open(uploaded_file)
-                        st.image(image, caption=f"Image {i+1}", use_column_width=True)
+                        st.image(image, caption=f"Image {i+1}", use_container_width=True)
                 
                 # Add analyze button for batch processing
                 if st.button("Analyze All Images"):
@@ -1116,7 +1204,7 @@ def main():
             
             # If it's an image file, display it
             if uploaded_file.type.startswith('image/'):
-                st.image(uploaded_file, caption="Uploaded Document", use_column_width=True)
+                st.image(uploaded_file, caption="Uploaded Document", use_container_width=True)
             else:
                 st.info("PDF document uploaded (preview not available)")