Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import cv2
+import os
+import pickle  # For loading the face embeddings database
+
+# Import your face detection and recognition functions
+from face_detection import detect_faces
+from face_recognition import recognize_face  # Assuming you have this
+
+# --- Configuration ---
+DATABASE_PATH = "face_database.pkl" # Path to your stored face embeddings
+THRESHOLD = 0.6  # Similarity threshold for recognition
+
+# --- Load the face embeddings database (if it exists) ---
+face_embeddings_db = {}
+if os.path.exists(DATABASE_PATH):
+    with open(DATABASE_PATH, 'rb') as f:
+        face_embeddings_db = pickle.load(f)
+
+# --- Function to process an input image ---
+def recognize_faces_in_image(image):
+    img_array = np.array(image)
+    detected_faces = detect_faces(img_array) # Returns a list of (box, confidence)
+
+    output_image = img_array.copy()
+    results = []
+
+    for box, _ in detected_faces:
+        x1, y1, w, h = box
+        x2, y2 = x1 + w, y1 + h
+
+        face_roi = img_array[y1:y2, x1:x2]
+
+        if face_roi.shape[0] > 0 and face_roi.shape[1] > 0:
+            identity = recognize_face(face_roi, face_embeddings_db, THRESHOLD)
+
+            if identity:
+                color = (0, 255, 0)  # Green for recognized
+                text = identity
+            else:
+                color = (255, 0, 0)  # Red for unknown
+                text = "Unknown"
+
+            cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(output_image, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
+            results.append(f"Face detected at ({x1}, {y1}) - {text}")
+        else:
+            results.append(f"Small or invalid face detected.")
+
+    return [Image.fromarray(output_image), "\n".join(results)]
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=recognize_faces_in_image,
+    inputs=gr.Image(label="Upload an Image"),
+    outputs=[gr.Image(label="Detected Faces"), gr.Textbox(label="Recognition Results")],
+    title="Face Recognition App",
+    description="Upload an image and see the detected and recognized faces."
+)
+
+iface.launch()