Update app.py

app.py

@@ -2,7 +2,8 @@ import streamlit as st
 import cv2
 import numpy as np
 from deepface import DeepFace
-import time
+from PIL import Image
+import io
 
 st.set_page_config(
     page_title="✨ Age & Gender Predictor",
@@ -14,153 +15,79 @@ st.title("✨ Age & Gender Predictor")
 st.write(
     """
     Welcome to the future of facial analysis!  
-    **Enable your webcam** and let our cutting-edge AI reveal your age and gender with impressive precision.
+    **Take a snapshot with your webcam** and let our cutting-edge AI reveal your age and gender with impressive precision.
     
     **No data is stored**.
     """
 )
 
-# Initialize session state variables if they don't exist
-if 'camera_on' not in st.session_state:
-    st.session_state.camera_on = False
-if 'captured_image' not in st.session_state:
-    st.session_state.captured_image = None
-if 'analyzed' not in st.session_state:
-    st.session_state.analyzed = False
+# Initialize session state for analysis results
+if 'age' not in st.session_state:
+    st.session_state.age = None
+if 'gender' not in st.session_state:
+    st.session_state.gender = None
+if 'gender_confidence' not in st.session_state:
+    st.session_state.gender_confidence = None
 
-# Function to start webcam
-def start_webcam():
-    st.session_state.camera_on = True
-    st.session_state.analyzed = False
+# Streamlit's built-in webcam capture
+img_file_buffer = st.camera_input("Take a picture with your webcam")
 
-# Function to capture image
-def capture_image():
-    st.session_state.camera_on = False
-    st.session_state.analyzed = False
-
-# Function to reset
-def reset_app():
-    st.session_state.camera_on = False
-    st.session_state.captured_image = None
-    st.session_state.analyzed = False
-
-# Create placeholders for webcam and captured image
-webcam_placeholder = st.empty()
-captured_image_placeholder = st.empty()
-
-# Create columns for buttons
-col1, col2 = st.columns(2)
-
-# Results area
-results_area = st.container()
-
-# Main app logic
-if not st.session_state.camera_on and st.session_state.captured_image is None:
-    # Initial state - just show the start button
-    if col1.button("Start Webcam"):
-        start_webcam()
-        st.rerun()
-
-elif st.session_state.camera_on:
-    # Webcam is on - show the capture and stop buttons
-    if col1.button("Stop Webcam"):
-        st.session_state.camera_on = False
-        st.rerun()
-    
-    if col2.button("Capture Image"):
-        capture_image()
-        st.rerun()
-    
-    # Display webcam feed
-    cap = cv2.VideoCapture(0)
-    
-    if not cap.isOpened():
-        st.error("Could not open webcam. Please check your camera connection.")
-    else:
-        # Get a frame
-        ret, frame = cap.read()
-        if ret:
-            # Convert to RGB for display
-            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            # Display the frame
-            webcam_placeholder.image(frame_rgb, channels="RGB", caption="Webcam Feed", use_column_width=True)
-            # Store the current frame
-            st.session_state.captured_image = frame.copy()
-        else:
-            st.error("Failed to capture image from webcam.")
-        
-        # Release the camera
-        cap.release()
-        
-        # Force a rerun to get another frame (creates a video effect)
-        if st.session_state.camera_on:
-            time.sleep(0.1)
-            st.rerun()
-
-elif st.session_state.captured_image is not None:
-    # We have a captured image - show it and analyze
-    if col1.button("Restart Webcam"):
-        start_webcam()
-        st.rerun()
-        
-    if col2.button("Reset"):
-        reset_app()
-        st.rerun()
-    
-    # Get the captured image
-    image = st.session_state.captured_image
-    
-    # Convert to RGB for display
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    
-    # Show the captured image
-    captured_image_placeholder.image(image_rgb, caption="Captured Image", use_column_width=True)
+# If an image was captured
+if img_file_buffer is not None:
+    # Convert the image buffer to a CV2 image
+    bytes_data = img_file_buffer.getvalue()
+    img_array = np.frombuffer(bytes_data, np.uint8)
+    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
     
-    # Analyze the image if not already analyzed
-    if not st.session_state.analyzed:
-        with results_area:
-            with st.spinner("Analyzing your image with advanced AI models..."):
-                try:
-                    results = DeepFace.analyze(
-                        image,
-                        actions=['age', 'gender'],
-                        detector_backend='retinaface',
-                        enforce_detection=True,
-                        align=True
-                    )
-                    
-                    if isinstance(results, list):
-                        results = sorted(results, key=lambda x: x.get('face_confidence', 0), reverse=True)
-                        main_result = results[0]
-                    else:
-                        main_result = results
-                    
-                    st.success("Analysis complete! Here's what we found:")
-                    st.write("## Detailed Results")
-                    
-                    age = main_result['age']
-                    st.write(f"**Predicted Age:** {age} years")
-                    
-                    gender = main_result['gender']
-                    dominant_gender = gender if isinstance(gender, str) else max(gender, key=gender.get)
-                    confidence = gender[dominant_gender] if isinstance(gender, dict) else None
-                    
-                    if confidence:
-                        st.write(f"**Predicted Gender:** {dominant_gender} ({confidence:.2f}% confidence)")
-                    else:
-                        st.write(f"**Predicted Gender:** {dominant_gender}")
-                    
-                    # Set analyzed flag to avoid re-analyzing on reruns
-                    st.session_state.analyzed = True
-                
-                except Exception as e:
-                    st.error(f"Analysis failed: {str(e)}")
-                    st.info(
-                        "For best results, please try the following tips:\n"
-                        "- Ensure good lighting conditions\n"
-                        "- Position your face clearly in the frame\n"
-                        "- Move closer to the camera if needed"
-                    )
+    # Display a spinner while analyzing
+    with st.spinner("Analyzing your image with advanced AI models..."):
+        try:
+            # Analyze the image using DeepFace
+            results = DeepFace.analyze(
+                img,
+                actions=['age', 'gender'],
+                detector_backend='retinaface',
+                enforce_detection=True,
+                align=True
+            )
+            
+            # Process results
+            if isinstance(results, list) and len(results) > 0:
+                results = sorted(results, key=lambda x: x.get('face_confidence', 0), reverse=True)
+                main_result = results[0]
+            else:
+                main_result = results
+            
+            # Store results in session state
+            st.session_state.age = main_result['age']
+            st.session_state.gender = main_result['gender']
+            
+            # Handle gender confidence if available
+            if isinstance(main_result['gender'], dict):
+                dominant_gender = max(main_result['gender'], key=main_result['gender'].get)
+                st.session_state.gender = dominant_gender
+                st.session_state.gender_confidence = main_result['gender'][dominant_gender]
+            
+            # Display success message
+            st.success("Analysis complete! Here's what we found:")
+            
+            # Display detailed results
+            st.write("## Detailed Results")
+            st.write(f"**Predicted Age:** {st.session_state.age} years")
+            
+            if st.session_state.gender_confidence:
+                st.write(f"**Predicted Gender:** {st.session_state.gender} ({st.session_state.gender_confidence:.2f}% confidence)")
+            else:
+                st.write(f"**Predicted Gender:** {st.session_state.gender}")
+            
+        except Exception as e:
+            st.error(f"Analysis failed: {str(e)}")
+            st.info(
+                "For best results, please try the following tips:\n"
+                "- Ensure good lighting conditions\n"
+                "- Position your face clearly in the frame\n"
+                "- Move closer to the camera if needed"
+            )
 
 st.markdown("---")
 st.markdown(