Delete pages/dashboard.py

pages/dashboard.py

@@ -1,124 +0,0 @@
-import gradio as gr
-import sqlite3
-import re
-import bcrypt
-import numpy as np
-import cv2
-from PIL import Image
-import tensorflow as tf
-import os
-import warnings
-
-# Suppress all warnings
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-warnings.filterwarnings("ignore")
-np.seterr(all='ignore')
-
-# Load model
-deepfake_model = tf.keras.models.load_model("model_15_64.h5")
-
-
-# Setup SQLite instead of MySQL
-conn = sqlite3.connect("users.db", check_same_thread=False)
-cursor = conn.cursor()
-
-# Create user_details table in SQLite
-cursor.execute('''
-CREATE TABLE IF NOT EXISTS user_details (
-    id INTEGER PRIMARY KEY AUTOINCREMENT,
-    NAME TEXT,
-    PHONE TEXT,
-    EMAIL TEXT UNIQUE,
-    GENDER TEXT,
-    PASSWORD TEXT
-)
-''')
-conn.commit()
-
-# Validation utilities
-def is_valid_email(email):
-    return re.match(r"[^@]+@[^@]+\.[^@]+", email)
-
-def is_valid_phone(phone):
-    return re.match(r"^[0-9]{10}$", phone)
-
-def preprocess_image(image):
-    image = np.array(image)
-    image = cv2.resize(image, (128, 128))
-    image = image.astype(np.float32) / 255.0
-    return np.expand_dims(image, axis=0)
-
-def predict_image(image):
-    preprocessed = preprocess_image(image)
-    prediction = deepfake_model.predict(preprocessed)[0][0]
-    return "✅ Real Image" if prediction >= 0.5 else "⚠️ Fake Image"
-
-# Register user
-def register_user(name, phone, email, password):
-    if not is_valid_email(email):
-        return "❌ Invalid email", False
-    if not is_valid_phone(phone):
-        return "❌ Phone must be 10 digits", False
-
-    cursor.execute("SELECT * FROM user_details WHERE EMAIL = ?", (email,))
-    if cursor.fetchone():
-        return "⚠️ Email already registered", False
-
-    hashed_pw = bcrypt.hashpw(password.encode(), bcrypt.gensalt())
-    cursor.execute("INSERT INTO user_details (NAME, PHONE, EMAIL, GENDER, PASSWORD) VALUES (?, ?, ?, ?, ?)",
-                   (name, phone, email, "U", hashed_pw))
-    conn.commit()
-    return "✅ Registration successful! Please log in.", True
-
-# Login user
-def login_user(email, password):
-    cursor.execute("SELECT PASSWORD FROM user_details WHERE EMAIL = ?", (email,))
-    result = cursor.fetchone()
-    if result and bcrypt.checkpw(password.encode(), result[0].encode() if isinstance(result[0], str) else result[0]):
-        return "✅ Login successful!", True
-    return "❌ Invalid credentials", False
-
-# App layout
-with gr.Blocks() as demo:
-    session = gr.State({})
-    show_login = gr.State(True)
-
-    status = gr.Textbox(label="", interactive=False)
-
-    with gr.Column(visible=True) as login_panel:
-        gr.Markdown("### Login or Sign Up")
-        name = gr.Textbox(label="Name (Sign Up Only)")
-        phone = gr.Textbox(label="Phone (Sign Up Only)")
-        email = gr.Textbox(label="Email")
-        password = gr.Textbox(label="Password", type="password")
-
-        login_btn = gr.Button("Login")
-        signup_btn = gr.Button("Sign Up")
-
-    with gr.Column(visible=False) as prediction_panel:
-        gr.Markdown("## Upload Image for Deepfake Detection")
-        image_input = gr.Image(type="pil")
-        result = gr.Textbox(label="Result")
-        predict_btn = gr.Button("Predict")
-        logout_btn = gr.Button("Logout")
-
-    # Logic
-    def handle_login(e, p):
-        msg, ok = login_user(e, p)
-        return msg, gr.update(visible=not ok), gr.update(visible=ok)
-
-    def handle_signup(n, ph, e, p):
-        msg, ok = register_user(n, ph, e, p)
-        return msg
-
-    def handle_logout():
-        return {}, gr.update(visible=True), gr.update(visible=False)
-
-    login_btn.click(handle_login, [email, password], [status, login_panel, prediction_panel])
-    signup_btn.click(handle_signup, [name, phone, email, password], status)
-    predict_btn.click(predict_image, inputs=image_input, outputs=result)
-    logout_btn.click(handle_logout, outputs=[session, login_panel, prediction_panel])
-
-# Launch
-if __name__ == "__main__":
-    demo.launch()