Deploying Gradio face recognition app

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import pickle
+import face_recognition
+
+# Load trained model
+def load_trained_model():
+    with open("face_embeddings.pkl", "rb") as f:
+        data = pickle.load(f)
+    return data["encodings"], data["names"]
+
+def recognize_face(frame):
+    encodings, names = load_trained_model()
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    faces = face_recognition.face_locations(rgb_frame)
+    face_encodings = face_recognition.face_encodings(rgb_frame, faces)
+
+    for face_encoding, face_location in zip(face_encodings, faces):
+        distances = face_recognition.face_distance(encodings, face_encoding)
+        min_distance = min(distances)
+        if min_distance < 0.6:
+            match_index = distances.argmin()
+            name = names[match_index]
+        else:
+            name = "Unknown"
+
+        top, right, bottom, left = face_location
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
+        cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+
+    return frame
+
+# Gradio Interface
+def process_image(image):
+    return recognize_face(image)
+
+iface = gr.Interface(fn=process_image, inputs="image", outputs="image", title="Face Identification System")
+iface.launch()

face_embeddings.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f9cef7ea99f8dd494ea4b1b18ece5010aaf6c77ac65f88cec161bd8534908809
+size 18243204

requirements.txt

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+gradio
+opencv-python
+numpy
+face-recognition
+pickle5