Update app.py

app.py

@@ -20,13 +20,13 @@ warnings.filterwarnings("ignore", category=UserWarning)
 
 # App title and description
 st.set_page_config(
-    page_title="Deepfake Analyzer", 
+    page_title="Deepfake Image Analyser", 
     layout="wide",
     page_icon="🔍"
 )
 
 # Main title and description
-st.title("Advanced Deepfake Image Analyzer")
+st.title("Deepfake Image Analyser")
 st.markdown("Analyze images for deepfake manipulation with multi-stage analysis")
 
 # Check for GPU availability
@@ -42,25 +42,9 @@ def check_gpu():
 # Sidebar components
 st.sidebar.title("Options")
 
-# Temperature slider
-temperature = st.sidebar.slider(
-    "Temperature", 
-    min_value=0.1, 
-    max_value=1.0, 
-    value=0.7, 
-    step=0.1,
-    help="Higher values make output more random, lower values more deterministic"
-)
-
-# Max response length slider
-max_tokens = st.sidebar.slider(
-    "Maximum Response Length", 
-    min_value=100, 
-    max_value=1000, 
-    value=500, 
-    step=50,
-    help="The maximum number of tokens in the response"
-)
+# Fixed values instead of sliders
+temperature = 0.7  # Fixed temperature value
+max_tokens = 500   # Fixed max tokens value
 
 # Custom instruction text area in sidebar
 custom_instruction = st.sidebar.text_area(
@@ -558,7 +542,11 @@ def load_llm_model():
             return None, None
 
 # Analyze image function
-def analyze_image_with_llm(image, gradcam_overlay, face_box, pred_label, confidence, question, model, tokenizer, temperature=0.7, max_tokens=500, custom_instruction=""):
+def analyze_image_with_llm(image, gradcam_overlay, face_box, pred_label, confidence, question, model, tokenizer, custom_instruction=""):
+    # Use fixed values for temperature and max_tokens
+    temperature = 0.7  # Fixed temperature value
+    max_tokens = 500   # Fixed max tokens value
+    
     # Create a prompt that includes GradCAM information
     if custom_instruction.strip():
         full_prompt = f"{question}\n\nThe image has been processed with GradCAM and classified as {pred_label} with confidence {confidence:.2f}. Focus on the highlighted regions in red/yellow which show the areas the detection model found suspicious.\n\n{custom_instruction}"
@@ -773,150 +761,4 @@ def main():
                         st.session_state.current_pred_label = pred_label
                         st.session_state.current_confidence = confidence
                         
-                        st.success("✅ Initial detection and GradCAM visualization complete!")
-                else:
-                    st.warning("⚠️ Please load the CLIP model first to perform initial detection.")
-            except Exception as e:
-                st.error(f"Error processing image: {str(e)}")
-                import traceback
-                st.error(traceback.format_exc())  # This will show the full error traceback
-    
-    # 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])
-            
-            with col1:
-                # Display original image and overlay side by side with controlled size
-                st.image(st.session_state.current_image, caption="Original Image", width=300)
-                if hasattr(st.session_state, 'current_overlay'):
-                    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")
-                    st.markdown(st.session_state.image_caption)
-                    st.markdown("---")
-                
-                # GradCAM analysis
-                if hasattr(st.session_state, 'gradcam_caption'):
-                    st.markdown("### GradCAM Analysis")
-                    st.markdown(st.session_state.gradcam_caption)
-    
-    # LLM Analysis section - AFTER Image Analysis Summary
-    with st.expander("Stage 3: Detailed Analysis with Vision LLM", expanded=False):
-        if hasattr(st.session_state, 'current_image') and st.session_state.llm_model_loaded:
-            st.subheader("Detailed Deepfake Analysis")
-            
-            # Display chat history
-            for i, (question, answer) in enumerate(st.session_state.chat_history):
-                st.markdown(f"**Question {i+1}:** {question}")
-                st.markdown(f"**Answer:** {answer}")
-                st.markdown("---")
-            
-            # Include both captions in the prompt if available
-            caption_text = ""
-            if hasattr(st.session_state, 'image_caption'):
-                caption_text += f"\n\nImage Description:\n{st.session_state.image_caption}"
-                
-            if hasattr(st.session_state, 'gradcam_caption'):
-                caption_text += f"\n\nGradCAM Analysis:\n{st.session_state.gradcam_caption}"
-            
-            # Default question with option to customize
-            default_question = f"This image has been classified as {st.session_state.current_pred_label}. Analyze the key features that led to this classification, focusing on the highlighted areas in the GradCAM visualization. Provide both a technical explanation for experts and a simple explanation for non-technical users."
-            
-            # User input for new question
-            new_question = st.text_area("Ask a question about the image:", value=default_question if not st.session_state.chat_history else "", height=100)
-            
-            # Analyze button and Clear Chat button in the same row
-            col1, col2 = st.columns([3, 1])
-            with col1:
-                analyze_button = st.button("🔍 Send Question", type="primary")
-            with col2:
-                clear_button = st.button("🗑️ Clear Chat History")
-            
-            if clear_button:
-                st.session_state.chat_history = []
-                st.experimental_rerun()
-                
-            if analyze_button and new_question:
-                try:
-                    # Add caption info if it's the first question
-                    if not st.session_state.chat_history:
-                        full_question = new_question + caption_text
-                    else:
-                        full_question = new_question
-                    
-                    result = analyze_image_with_llm(
-                        st.session_state.current_image,
-                        st.session_state.current_overlay,
-                        st.session_state.current_face_box,
-                        st.session_state.current_pred_label,
-                        st.session_state.current_confidence,
-                        full_question,
-                        st.session_state.llm_model,
-                        st.session_state.tokenizer,
-                        temperature=temperature,
-                        max_tokens=max_tokens,
-                        custom_instruction=custom_instruction
-                    )
-                    
-                    # Add to chat history
-                    st.session_state.chat_history.append((new_question, result))
-                    
-                    # Display the latest result too
-                    st.success("✅ Analysis complete!")
-                    
-                    # Check if the result contains both technical and non-technical explanations
-                    if "Technical" in result and "Non-Technical" in result:
-                        try:
-                            # Split the result into technical and non-technical sections
-                            parts = result.split("Non-Technical")
-                            technical = parts[0]
-                            non_technical = "Non-Technical" + parts[1]
-                            
-                            # Display in two columns
-                            tech_col, simple_col = st.columns(2)
-                            with tech_col:
-                                st.subheader("Technical Analysis")
-                                st.markdown(technical)
-                            
-                            with simple_col:
-                                st.subheader("Simple Explanation")
-                                st.markdown(non_technical)
-                        except Exception as e:
-                            # Fallback if splitting fails
-                            st.subheader("Analysis Result")
-                            st.markdown(result)
-                    else:
-                        # Just display the whole result
-                        st.subheader("Analysis Result")
-                        st.markdown(result)
-                    
-                    # Rerun to update the chat history display
-                    st.experimental_rerun()
-                    
-                except Exception as e:
-                    st.error(f"Error during LLM analysis: {str(e)}")
-                    
-        elif not hasattr(st.session_state, 'current_image'):
-            st.warning("⚠️ Please upload an image and complete the initial detection first.")
-        else:
-            st.warning("⚠️ Please load the Vision LLM to perform detailed analysis.")
-    
-    # Footer
-    st.markdown("---")
-    st.caption("Advanced Deepfake Image Analyzer with Structured BLIP Captioning")
-
-if __name__ == "__main__":
-    main()
+                        st.success("✅ Initial detection and GradCAM visualization complete!")