Update app.py

app.py

@@ -40,22 +40,7 @@ def check_gpu():
         return False
 
 # Sidebar components
-st.sidebar.title("Options")
-
-# Fixed values for temperature and max tokens
-temperature = 0.7
-max_tokens = 500
-
-# Custom instruction text area in sidebar
-custom_instruction = st.sidebar.text_area(
-    "Custom Instructions (Advanced)",
-    value="Focus on analyzing the highlighted regions from the GradCAM visualization. Examine facial inconsistencies, lighting irregularities, and other artifacts visible in the heat map.",
-    help="Add specific instructions for the LLM analysis"
-)
-
-# About section in sidebar
-st.sidebar.markdown("---")
-st.sidebar.subheader("About")
+st.sidebar.title("About")
 st.sidebar.markdown("""
 This analyzer performs multi-stage detection:
 1. **Initial Detection**: CLIP-based classifier
@@ -72,6 +57,17 @@ The system looks for:
 - Blending problems
 """)
 
+# Fixed values for temperature and max tokens
+temperature = 0.7
+max_tokens = 500
+
+# Custom instruction text area in sidebar
+custom_instruction = st.sidebar.text_area(
+    "Custom Instructions (Advanced)",
+    value="Focus on analyzing the highlighted regions from the GradCAM visualization. Examine facial inconsistencies, lighting irregularities, and other artifacts visible in the heat map.",
+    help="Add specific instructions for the LLM analysis"
+)
+
 # ----- GradCAM Implementation -----
 
 class ImageDataset(torch.utils.data.Dataset):
@@ -768,8 +764,6 @@ def main():
     # Image Analysis Summary section - AFTER Stage 2
     if hasattr(st.session_state, 'current_image') and (hasattr(st.session_state, 'image_caption') or hasattr(st.session_state, 'gradcam_caption')):
         with st.expander("Image Analysis Summary", expanded=True):
-            st.subheader("Generated Descriptions and Analysis")
-            
             # Display image, captions, and results in organized layout with proper formatting
             col1, col2 = st.columns([1, 2])
             
@@ -780,12 +774,6 @@ def main():
                     st.image(st.session_state.current_overlay, caption="GradCAM Overlay", width=300)
             
             with col2:
-                # Detection result
-                if hasattr(st.session_state, 'current_pred_label'):
-                    st.markdown("### Detection Result")
-                    st.markdown(f"**Classification:** {st.session_state.current_pred_label} (Confidence: {st.session_state.current_confidence:.2%})")
-                    st.markdown("---")
-                
                 # Image description
                 if hasattr(st.session_state, 'image_caption'):
                     st.markdown("### Image Description")