Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from PIL import Image
+import cv2
 import os
 import base64
 import io
@@ -20,6 +21,7 @@ PAGE_CONFIG = {
 }
 
 ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
+ALLOWED_VIDEO_TYPES = ['mp4', 'avi', 'mov']
 
 CSS_STYLES = """
 <style>
@@ -79,6 +81,32 @@ def process_image_data(uploaded_file):
         st.error(f"Image processing error: {str(e)}")
         return None, None
 
+def extract_video_frames(uploaded_video):
+    """Extract frames from uploaded video for analysis"""
+    try:
+        tfile = io.BytesIO(uploaded_video.read())
+        temp_filename = "temp_video.mp4"
+        with open(temp_filename, "wb") as f:
+            f.write(tfile.getvalue())
+        
+        cap = cv2.VideoCapture(temp_filename)
+        frame_list = []
+        frame_count = 0
+        
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret or frame_count > 10:  # Process only up to 10 frames
+                break
+            frame_list.append(frame)
+            frame_count += 1
+        
+        cap.release()
+        os.remove(temp_filename)
+        return frame_list
+    except Exception as e:
+        st.error(f"Video processing error: {str(e)}")
+        return []
+
 def generate_pdf_report(report_text):
     """Generate PDF document from report text"""
     buffer = io.BytesIO()
@@ -97,15 +125,13 @@ def generate_pdf_report(report_text):
     buffer.seek(0)
     return buffer
 
-def generate_rice_report(uploaded_file, client):
+def generate_rice_report(image_data, img_format, client):
     """Generate AI-powered rice quality analysis"""
-    base64_image, img_format = process_image_data(uploaded_file)
-    
-    if not base64_image:
+    if not image_data:
         return None
-
-    image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
-
+    
+    image_url = f"data:image/{img_format.lower()};base64,{image_data}"
+    
     try:
         response = client.chat.completions.create(
             model="llama-3.2-11b-vision-preview",
@@ -141,62 +167,40 @@ def display_main_interface():
     st.subheader("AI-Powered Rice Grain Inspection")
     st.markdown("---")
 
-    # Display analysis results
-    if st.session_state.get('analysis_result'):
-        st.markdown("### 📋 Analysis Report")
-        st.markdown(
-            f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>', 
-            unsafe_allow_html=True
-        )
-        pdf_report = generate_pdf_report(st.session_state.analysis_result)
-        st.download_button(
-            label="📄 Download PDF Report",
-            data=pdf_report,
-            file_name="rice_quality_report.pdf",
-            mime="application/pdf"
-        )
-
-    if st.button("Clear Analysis 🗑️"):
-        st.session_state.pop('analysis_result', None)
-        st.rerun()
-
 def render_sidebar(client):
     """Create sidebar interface elements"""
     with st.sidebar:
-        st.markdown("### Features")
-        st.markdown("""
-        - **Rice Type Classification** (e.g., Basmati, Jasmine, Indica)
-        - **Quality Check** (Broken grains %, impurities %, discoloration %)
-        - **Foreign Object Detection** (Husks, stones, debris)
-        - **Grain Size & Shape Analysis**
-        - **Processing Recommendations**
-        """)
-        st.markdown("---")
-        
-        st.subheader("Upload Rice Image")
-        uploaded_file = st.file_uploader(
-            "Select an image of rice grains", 
-            type=ALLOWED_FILE_TYPES
-        )
+        st.subheader("Upload Image or Video")
+        uploaded_file = st.file_uploader("Select an image or video", type=ALLOWED_FILE_TYPES + ALLOWED_VIDEO_TYPES)
         
         if uploaded_file:
-            st.image(Image.open(uploaded_file), caption="Uploaded Image", use_column_width=True)
-            if st.button("Analyze Rice Quality 🔍"):
-                with st.spinner("Processing image... This may take a few seconds."):
-                    report = generate_rice_report(uploaded_file, client)
-                    st.session_state.analysis_result = report
-                    st.rerun()
+            file_type = uploaded_file.type
+            if "video" in file_type:
+                frames = extract_video_frames(uploaded_file)
+                if frames:
+                    st.image(frames[0], caption="Extracted Frame for Analysis", use_column_width=True)
+                    frame_img = Image.fromarray(cv2.cvtColor(frames[0], cv2.COLOR_BGR2RGB))
+                    buffer = io.BytesIO()
+                    frame_img.save(buffer, format="JPEG")
+                    base64_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
+                    report = generate_rice_report(base64_image, "jpeg", client)
+            else:
+                st.image(Image.open(uploaded_file), caption="Uploaded Image", use_column_width=True)
+                base64_image, img_format = process_image_data(uploaded_file)
+                report = generate_rice_report(base64_image, img_format, client)
+            
+            if report:
+                st.markdown("### 📋 Analysis Report")
+                st.markdown(report)
 
 # ======================
 # APPLICATION ENTRYPOINT
 # ======================
 def main():
-    """Primary application controller"""
     configure_application()
     groq_client = initialize_api_client()
-    
     display_main_interface()
     render_sidebar(groq_client)
 
 if __name__ == "__main__":
-    main()
+    main()