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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from deepface import DeepFace | |
| from PIL import Image | |
| import io | |
| st.set_page_config( | |
| page_title="✨ Age & Gender Predictor", | |
| page_icon=":sparkles:", | |
| layout="centered", | |
| ) | |
| st.title("✨ Age & Gender Predictor") | |
| st.write( | |
| """ | |
| Welcome to the future of facial analysis! | |
| **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 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 | |
| # Streamlit's built-in webcam capture | |
| img_file_buffer = st.camera_input("Take a picture with your webcam") | |
| # 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) | |
| # 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( | |
| """ | |
| **Powered by DeepFace & RetinaFace** | |
| """ | |
| ) |