Update app.py

app.py

@@ -1,63 +1,122 @@
+import os
+import cv2
+import numpy as np
+from keras_facenet import FaceNet
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+
+# Initialize FaceNet model
+embedder = FaceNet()
+
+def img_to_encoding(image, embedder):
+    try:
+        if image is None:
+            print("Debug: Image is None")
+            return None
+        if not isinstance(image, np.ndarray):
+            print(f"Debug: Image is not a numpy array, type(image)={type(image)}")
+            return None
+
+        print(f"Debug: Image shape = {image.shape}, dtype = {image.dtype}")
+
+        # Convert BGR to RGB and resize to 160x160
+        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        img = cv2.resize(img, (160, 160))
+
+        # Get the embedding
+        embedding = embedder.embeddings([img])[0]
+        return embedding
+    except Exception as e:
+        print(f"Error in img_to_encoding: {e}")
+        return None
+
+database = {}
+
+def register_user(image, username):
+    try:
+        if not os.path.exists('images'):
+            os.makedirs('images')
+        
+        if image is None or image.size == 0:
+            print("Debug: Invalid image input")
+            return "Error: Uploaded image is empty or invalid."
+
+        print(f"Debug: Image received for registration, username={username}")
+
+        # Save the image to disk
+        img_path = f'images/{username}.jpg'
+        success = cv2.imwrite(img_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+        
+        if not success:
+            print("Debug: Failed to save image to disk")
+            return "Error: Failed to save the image."
+
+        encoding = img_to_encoding(image, embedder)
+        if encoding is None:
+            return "Error: Failed to process the image."
+
+        database[username] = encoding
+        return f"User '{username}' registered successfully!"
+    except Exception as e:
+        print(f"Error during registration: {e}")
+        return f"Error during registration: {e}"
+
+def verify_user(image):
+    try:
+        if image is None or image.size == 0:
+            print("Debug: Invalid image input for verification")
+            return "Error: Uploaded image is empty or invalid."
+
+        print("Debug: Image received for verification")
+
+        encoding = img_to_encoding(image, embedder)
+        if encoding is None:
+            return "Error: Failed to process the image."
+
+        min_dist = 1000
+        identity = None
+
+        for (name, db_enc) in database.items():
+            dist = np.linalg.norm(encoding - db_enc)
+            if dist < min_dist:
+                min_dist = dist
+                identity = name
+
+        if min_dist > 5:
+            return "Identity not recognized."
+        else:
+            return f"It's {identity}, the distance is {min_dist}."
+    except Exception as e:
+        print(f"Error during verification: {e}")
+        return f"Error during verification: {e}"
+
+# Gradio Interface for Registration
+register_interface = gr.Interface(
+    fn=register_user,
+    inputs=[gr.Image(type="numpy", label="Take a Photo or Upload"), gr.Textbox(label="Username")],
+    outputs="text",
+    title="Register User",
+    description="Take a photo or upload an image and enter a username to register."
 )
 
+# Gradio Interface for Verification
+verify_interface = gr.Interface(
+    fn=verify_user,
+    inputs=gr.Image(type="numpy", label="Take a Photo or Upload"),
+    outputs="text",
+    title="Verify User",
+    description="Take a photo or upload an image to verify the user's identity."
+)
+
+# Combine the interfaces into a single application
+app = gr.Blocks()
+
+with app:
+    gr.Markdown("# Face Identification System")
+    with gr.Tab("Register"):
+        register_interface.render()
+    with gr.Tab("Verify"):
+        verify_interface.render()
 
+# Run the Gradio application
 if __name__ == "__main__":
-    demo.launch()
+    app.launch()