Update app.py

app.py

@@ -617,25 +617,23 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    if uploaded_file is not None:
-        # Display original image
-        image_bytes = uploaded_file.getvalue()
-        image = Image.open(io.BytesIO(image_bytes))
-        col1, col2 = st.columns(2)
-        
-        with col1:
+if uploaded_file is not None:
+    image_bytes = uploaded_file.getvalue()
+    image = Image.open(io.BytesIO(image_bytes))
+    col1, col2 = st.columns(2)
+    
+    with col1:
             st.subheader("Original Image")
             st.image(image, use_container_width=True)
         
         # Process image
-        with st.spinner("Analyzing image..."):
+    with st.spinner("Analyzing image..."):
             result = process_image(image_bytes)
         
-        if result and 'data' in result:
+    if result and 'data' in result:
             data = result['data'][0]
-            
-            # Display results
-            if 'bounding_boxes' in data:
+        
+    if 'bounding_boxes' in data:
                 for box in data['bounding_boxes']:
                     # Draw bounding box on image
                     annotated_image = draw_bounding_box(
@@ -645,43 +643,43 @@ uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png
                         box['is_deepfake']
                     )
                     
-                    with col2:
-                        st.subheader("Analysis Result")
-                        st.image(annotated_image, use_container_width=True)
-                    
-                    # Display confidence metrics
-                    deepfake_conf = box['is_deepfake'] * 100
-                    bbox_conf = box['bbox_confidence'] * 100
-                    
-                    st.write("### Detection Confidence")
-                    col3, col4 = st.columns(2)
-                    
-                    with col3:
-                        st.metric("Deepfake Confidence", f"{deepfake_conf:.1f}%")
-                        st.progress(deepfake_conf/100)
-                    
-                    with col4:
-                        st.metric("Face Detection Confidence", f"{bbox_conf:.1f}%")
-                        st.progress(bbox_conf/100)
-                    
-                    if deepfake_conf > 90:
-                        st.error("⚠️ High probability of deepfake detected!")
-                    elif deepfake_conf > 70:
-                        st.warning("⚠️ Moderate probability of deepfake detected!")
-                    else:
-                        st.success("✅ Low probability of deepfake")
-                    
-                    # Display raw JSON data in expander
-                    with st.expander("View Raw JSON Response"):
-                        st.json(result)
-            else:
-                st.warning("No faces detected in the image")
+                with col2:
+                    st.subheader("Analysis Result")
+                    st.image(annotated_image, use_container_width=True)
+                
+                # Display confidence metrics
+                deepfake_conf = box['is_deepfake'] * 100
+                bbox_conf = box['bbox_confidence'] * 100
+                
+                st.write("### Detection Confidence")
+                col3, col4 = st.columns(2)
+                
+                with col3:
+                    st.metric("Deepfake Confidence", f"{deepfake_conf:.1f}%")
+                    st.progress(deepfake_conf/100)
+                
+                with col4:
+                    st.metric("Face Detection Confidence", f"{bbox_conf:.1f}%")
+                    st.progress(bbox_conf/100)
+                
+                if deepfake_conf > 90:
+                    st.error("⚠️ High probability of deepfake detected!")
+                elif deepfake_conf > 70:
+                    st.warning("⚠️ Moderate probability of deepfake detected!")
+                else:
+                    st.success("✅ Low probability of deepfake")
+                
+                # Display raw JSON data in expander
+                with st.expander("View Raw JSON Response"):
+                    st.json(result)
         else:
-            st.error("Failed to process image")
+            st.warning("No faces detected in the image")
+    else:
+        st.error("Failed to process image")
 
 def calculate_similarity(text1, text2):
-    matcher = SequenceMatcher(None, text1, text2)
-    return matcher.ratio()
+matcher = SequenceMatcher(None, text1, text2)
+return matcher.ratio()
 
 if __name__ == "__main__":
-    main()
+main()