app/Hackathon_setup/app.py

"""
Robust Hugging Face Space App for Face Recognition
"""

import gradio as gr
import numpy as np
import cv2
from PIL import Image
import warnings
import os

# Suppress all warnings
warnings.filterwarnings('ignore')

# Import the robust face recognition functions
from face_recognition import get_similarity, get_face_class

def predict_similarity(file1, file2):
    """Predict similarity between two face images"""
    try:
        if file1 is None or file2 is None:
            return "Please upload both images"
        
        # Load images
        img1 = cv2.imread(file1)
        img2 = cv2.imread(file2)
        
        if img1 is None or img2 is None:
            return "Error loading images. Please ensure they are valid image files."
        
        # Get similarity score
        similarity_score = get_similarity(img1, img2)
        
        if similarity_score == -1.0:
            return "Error processing images. Please try again."
        
        # Interpret the result
        if similarity_score > 0.8:
            result = f"Very High Similarity ({similarity_score:.4f}) - Likely same person"
        elif similarity_score > 0.6:
            result = f"High Similarity ({similarity_score:.4f}) - Possibly same person"
        elif similarity_score > 0.4:
            result = f"Moderate Similarity ({similarity_score:.4f}) - Uncertain"
        elif similarity_score > 0.2:
            result = f"Low Similarity ({similarity_score:.4f}) - Likely different persons"
        else:
            result = f"Very Low Similarity ({similarity_score:.4f}) - Definitely different persons"
        
        return result
        
    except Exception as e:
        return f"Error: {str(e)}"

def predict_class(file):
    """Predict the class of a face image"""
    try:
        if file is None:
            return "Please upload an image"
        
        # Load image
        img = cv2.imread(file)
        
        if img is None:
            return "Error loading image. Please ensure it's a valid image file."
        
        # Get face class
        face_class = get_face_class(img)
        
        if face_class.startswith("Error:"):
            return face_class
        
        return f"Predicted Face Class: {face_class}"
        
    except Exception as e:
        return f"Error: {str(e)}"

# Create Gradio interface
with gr.Blocks(title="Face Recognition System", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 👤 Face Recognition System")
    gr.Markdown("Upload face images to compare similarity or classify faces using advanced AI.")
    
    with gr.Tab("🔍 Face Similarity"):
        gr.Markdown("## Compare Two Faces")
        gr.Markdown("Upload two face images to compare their similarity using cosine similarity.")
        
        with gr.Row():
            with gr.Column():
                img1 = gr.Image(type="filepath", label="First Face Image", height=200)
            with gr.Column():
                img2 = gr.Image(type="filepath", label="Second Face Image", height=200)
        
        similarity_btn = gr.Button("🔍 Compare Faces", variant="primary", size="lg")
        similarity_output = gr.Textbox(label="Similarity Result", interactive=False, lines=3)
        
        similarity_btn.click(
            fn=predict_similarity,
            inputs=[img1, img2],
            outputs=similarity_output
        )
    
    with gr.Tab("🏷️ Face Classification"):
        gr.Markdown("## Classify a Face")
        gr.Markdown("Upload a face image to classify it into predefined categories.")
        
        img_class = gr.Image(type="filepath", label="Face Image", height=300)
        class_btn = gr.Button("🏷️ Classify Face", variant="primary", size="lg")
        class_output = gr.Textbox(label="Classification Result", interactive=False, lines=2)
        
        class_btn.click(
            fn=predict_class,
            inputs=[img_class],
            outputs=class_output
        )
    
    with gr.Tab("ℹ️ About"):
        gr.Markdown("""
        ## About This Face Recognition System
        
        This advanced face recognition system uses:
        - **🧠 Siamese Neural Network** for robust face feature extraction
        - **📐 Cosine Similarity** for accurate face comparison
        - **🎯 K-Nearest Neighbors** for face classification
        - **🛡️ Robust Error Handling** for reliable operation
        
        ### How to Use:
        1. **Face Similarity**: Upload two face images to compare their similarity
        2. **Face Classification**: Upload a single face image to classify it
        
        ### Similarity Score Interpretation:
        - **0.8-1.0**: Very High Similarity (likely same person) 🟢
        - **0.6-0.8**: High Similarity (possibly same person) 🟡
        - **0.4-0.6**: Moderate Similarity (uncertain) 🟠
        - **0.2-0.4**: Low Similarity (likely different persons) 🔴
        - **0.0-0.2**: Very Low Similarity (definitely different persons) ⚫
        
        ### Technical Features:
        - Handles various image formats
        - Automatic face detection and cropping
        - Robust error handling
        - Cross-platform compatibility
        - Optimized for production use
        """)

if __name__ == "__main__":
    demo.launch()