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Update dashboard.py
Browse files- dashboard.py +70 -45
dashboard.py
CHANGED
@@ -9,10 +9,10 @@ import tensorflow as tf
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import os
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import warnings
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# Import pages
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from pages import about, community, user_guide
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# Suppress
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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warnings.filterwarnings("ignore")
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np.seterr(all='ignore')
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@@ -20,12 +20,11 @@ np.seterr(all='ignore')
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# Load deepfake model
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deepfake_model = tf.keras.models.load_model("model_15_64.h5")
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#
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db_path = os.path.abspath("users.db")
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print(f"β
Using database at: {db_path}")
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conn = sqlite3.connect(db_path, check_same_thread=False)
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cursor = conn.cursor()
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS user_details (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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@@ -38,13 +37,14 @@ CREATE TABLE IF NOT EXISTS user_details (
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''')
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conn.commit()
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#
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def is_valid_email(email):
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return re.match(r"[^@]+@[^@]+\.[^@]+", email)
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def is_valid_phone(phone):
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return re.match(r"^[0-9]{10}$", phone)
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def preprocess_image(image):
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image = np.array(image)
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image = cv2.resize(image, (128, 128))
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@@ -56,78 +56,55 @@ def predict_image(image):
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prediction = deepfake_model.predict(preprocessed)[0][0]
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return "β
Real Image" if prediction >= 0.5 else "β οΈ Fake Image"
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def register_user(name, phone, email, password):
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if not is_valid_email(email):
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return "β Invalid email"
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if not is_valid_phone(phone):
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return "β Phone must be 10 digits"
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cursor.execute("SELECT * FROM user_details WHERE EMAIL = ?", (email,))
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if cursor.fetchone():
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return "β οΈ Email already registered"
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hashed_pw = bcrypt.hashpw(password.encode(), bcrypt.gensalt())
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cursor.execute("INSERT INTO user_details (NAME, PHONE, EMAIL, GENDER, PASSWORD) VALUES (?, ?, ?, ?, ?)",
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(name, phone, email, "U", hashed_pw))
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conn.commit()
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print(f"β
Registered new user: {email}")
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return "β
Registration successful! Please log in."
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def login_user(email, password):
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cursor.execute("SELECT PASSWORD FROM user_details WHERE EMAIL = ?", (email,))
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result = cursor.fetchone()
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if result and bcrypt.checkpw(password.encode(), result[0]):
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return
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return
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# Gradio App
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with gr.Blocks() as demo:
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with gr.Tabs() as tabs:
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with gr.Tab("π Login"):
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gr.Markdown("### Login or Sign Up")
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status = gr.Textbox(label="Status", interactive=False)
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name = gr.Textbox(label="Name (Sign Up Only)")
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phone = gr.Textbox(label="Phone (Sign Up Only)")
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email = gr.Textbox(label="Email")
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password = gr.Textbox(label="Password", type="password")
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login_btn = gr.Button("Login")
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signup_btn = gr.Button("Sign Up")
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def handle_signup(n, ph, e, p):
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msg, ok = register_user(n, ph, e, p)
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return msg, ok
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login_btn.click(handle_login, [email, password], [status, session])
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signup_btn.click(handle_signup, [name, phone, email, password], [status, session])
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with gr.Tab("π§ͺ Detect Deepfake") as detect_tab:
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with gr.Column(visible=False) as detection_content:
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gr.Markdown("### Upload an Image to Detect Deepfake")
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image_input = gr.Image(type="pil")
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result = gr.Textbox(label="Prediction Result")
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predict_btn = gr.Button("Predict")
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predict_btn.click(fn=predict_image, inputs=image_input, outputs=result)
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# Show warning if not logged in
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with gr.Column(visible=True) as login_prompt:
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warning_text = gr.Markdown("β οΈ Please login or sign up to access deepfake detection.")
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def toggle_tab(logged_in):
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return (
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gr.update(visible=logged_in), # detection_content
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gr.update(visible=not logged_in), # login_prompt
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)
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# Toggle detection tab visibility based on login state
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session.change(fn=toggle_tab, inputs=session, outputs=[detection_content, login_prompt])
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with gr.Tab("βΉοΈ About"):
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about.layout()
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@@ -138,6 +115,54 @@ with gr.Blocks() as demo:
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with gr.Tab("π User Guide"):
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user_guide.layout()
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if __name__ == "__main__":
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demo.launch()
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import os
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import warnings
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# Import content pages
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from pages import about, community, user_guide
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# Suppress TensorFlow and warning logs
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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warnings.filterwarnings("ignore")
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np.seterr(all='ignore')
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# Load deepfake model
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deepfake_model = tf.keras.models.load_model("model_15_64.h5")
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# Database setup
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db_path = os.path.abspath("users.db")
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print(f"β
Using database at: {db_path}")
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conn = sqlite3.connect(db_path, check_same_thread=False)
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cursor = conn.cursor()
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS user_details (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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''')
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conn.commit()
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# Validators
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def is_valid_email(email):
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return re.match(r"[^@]+@[^@]+\.[^@]+", email)
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def is_valid_phone(phone):
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return re.match(r"^[0-9]{10}$", phone)
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# Image preprocessing
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def preprocess_image(image):
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image = np.array(image)
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image = cv2.resize(image, (128, 128))
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prediction = deepfake_model.predict(preprocessed)[0][0]
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return "β
Real Image" if prediction >= 0.5 else "β οΈ Fake Image"
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# Auth logic
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def register_user(name, phone, email, password):
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if not is_valid_email(email):
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return "β Invalid email"
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if not is_valid_phone(phone):
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return "β Phone must be 10 digits"
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cursor.execute("SELECT * FROM user_details WHERE EMAIL = ?", (email,))
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if cursor.fetchone():
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return "β οΈ Email already registered"
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hashed_pw = bcrypt.hashpw(password.encode(), bcrypt.gensalt())
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cursor.execute("INSERT INTO user_details (NAME, PHONE, EMAIL, GENDER, PASSWORD) VALUES (?, ?, ?, ?, ?)",
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(name, phone, email, "U", hashed_pw))
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conn.commit()
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print(f"β
Registered new user: {email}")
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return "β
Registration successful! Please log in."
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def login_user(email, password):
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cursor.execute("SELECT PASSWORD FROM user_details WHERE EMAIL = ?", (email,))
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result = cursor.fetchone()
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if result and bcrypt.checkpw(password.encode(), result[0] if isinstance(result[0], bytes) else result[0].encode()):
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return True
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return False
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# Gradio UI
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with gr.Blocks() as demo:
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is_logged_in = gr.State(False)
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tab_index = gr.State(0)
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with gr.Tabs() as tabs:
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with gr.Tab("π Login"):
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gr.Markdown("### Login or Sign Up")
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name = gr.Textbox(label="Name (Sign Up Only)")
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phone = gr.Textbox(label="Phone (Sign Up Only)")
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email = gr.Textbox(label="Email")
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password = gr.Textbox(label="Password", type="password")
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login_btn = gr.Button("Login")
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signup_btn = gr.Button("Sign Up")
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message_output = gr.Markdown("", visible=False)
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with gr.Tab("π§ͺ Detect Deepfake", visible=False) as detect_tab:
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detect_area = gr.Column(visible=True)
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with detect_area:
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gr.Markdown("### Upload an Image to Detect Deepfake")
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image_input = gr.Image(type="pil")
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result = gr.Textbox(label="Prediction Result")
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predict_btn = gr.Button("Predict")
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predict_btn.click(fn=predict_image, inputs=image_input, outputs=result)
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detect_warning = gr.Markdown("β Please log in to use this feature.", visible=False)
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with gr.Tab("βΉοΈ About"):
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about.layout()
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with gr.Tab("π User Guide"):
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user_guide.layout()
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with gr.Tab("πͺ Logout", visible=False) as logout_tab:
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gr.Markdown("You are logged in.")
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logout_btn = gr.Button("Logout")
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# Handlers
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def handle_login(email, password):
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success = login_user(email, password)
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return (
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"β
Login successful!" if success else "β Invalid credentials",
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success,
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1 if success else 0,
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gr.update(visible=success),
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gr.update(visible=not success),
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gr.update(visible=success),
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gr.update(visible=True)
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)
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def handle_signup(name, phone, email, password):
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msg = register_user(name, phone, email, password)
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return gr.update(value=msg, visible=True)
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def handle_logout():
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return (
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False,
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0,
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gr.update(visible=False),
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False)
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)
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login_btn.click(
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fn=handle_login,
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inputs=[email, password],
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outputs=[message_output, is_logged_in, tab_index, detect_area, detect_warning, logout_tab, message_output]
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)
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signup_btn.click(
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fn=handle_signup,
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inputs=[name, phone, email, password],
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outputs=[message_output]
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)
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logout_btn.click(
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fn=handle_logout,
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inputs=[],
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outputs=[is_logged_in, tab_index, detect_area, detect_warning, logout_tab, message_output]
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)
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if __name__ == "__main__":
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demo.launch()
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