Delete pages/home.py

pages/home.py

@@ -1,123 +0,0 @@
-import streamlit as st
-import mysql.connector
-import re
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import cv2
-
-# Initialize database connection
-try:
-    mydb = mysql.connector.connect(
-        host="localhost",
-        user="root",
-        password="12345",
-        database="user_info"
-    )
-    mycursor = mydb.cursor()
-    print("Connection Established")
-except mysql.connector.Error as err:
-    print(f"Error: {err}")
-    st.error("Database connection failed.")
-
-# Load the deepfake detection model
-deepfake_model_path = "C:\\Users\\Paras Sharma\\OneDrive\\Documents\\Deepfake\\model_15_64 (1).h5"
-deepfake_model = tf.keras.models.load_model(deepfake_model_path)
-
-def validate_name(name):
-    if re.match(r"^[a-zA-Z]+\s[a-zA-Z]+$", name):
-        return True
-    else:
-        st.warning("Please enter a valid name (e.g., Firstname Lastname).")
-        return False
-
-def validate_phone(phone):
-    if re.match(r"^[0-9]{10}$", phone):
-        return True
-    else:
-        st.warning("Please enter a valid 10-digit phone number.")
-        return False
-
-def validate_email(email):
-    email_pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
-    if re.match(email_pattern, email):
-        return True
-    else:
-        st.warning("Please enter a valid email.")
-        return False
-
-def preprocess_image(image):
-    try:
-        image = np.array(image)
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
-        image = cv2.resize(image, (128, 128))  # Resize to match model input size
-        image = image.astype(np.float32) / 255.0  # Normalize pixel values
-        return np.expand_dims(image, axis=0)  # Add batch dimension
-    except Exception as e:
-        print(f"Error preprocessing image: {e}")
-        return None
-
-def predict_deepfake(image):
-    preprocessed_image = preprocess_image(image)
-    if preprocessed_image is not None:
-        prediction = deepfake_model.predict(preprocessed_image)
-        return prediction[0][0]  # Assuming the model outputs a single value between 0 and 1
-    else:
-        return None
-
-def show_home():
-    st.header(' ')
-    st.markdown("<h1 style='text-align: center; color: black;'>AuthentiTech: Leveraging Machine Learning to Combat Deepfake Detection</h1>", unsafe_allow_html=True)
-    st.header(' ', divider="rainbow")
-    st.header(' ')
-
-    st.markdown("<p style='font-size: medium;'>Enter Your Details</p>", unsafe_allow_html=True)
-
-    NAME = st.text_input('Name: ', st.session_state.get('name', ''))
-    if not validate_name(NAME):
-        return
-
-    PHONE = st.text_input('Contact Number(+91): ', max_chars=10)
-    PHONE = PHONE.strip()  # Remove any leading/trailing spaces
-    if not validate_phone(PHONE):
-        return
-
-    GENDER = st.selectbox('Enter gender', ('F', 'M', 'other'))
-
-    EMAIL = st.text_input('Email: ', st.session_state.get('EMAIL', ''))
-    if not validate_email(EMAIL):
-        return
-
-    if st.button("Submit"):
-        try:
-            sql = "INSERT INTO user_details (NAME, PHONE, EMAIL, GENDER) VALUES (%s, %s, %s, %s)"
-            val = (NAME, PHONE, EMAIL, GENDER)
-            mycursor.execute(sql, val)
-            mydb.commit()
-            st.session_state['name'] = NAME
-            st.session_state['EMAIL'] = EMAIL
-            st.success("Details submitted successfully!")
-        except mysql.connector.Error as err:
-            st.error(f"Error: {err}")
-            print(f"Error executing SQL: {err}")
-
-    st.write("Upload your image (JPEG, JPG, PNG) here (max size: 15 KB):")
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"], accept_multiple_files=False, key="file_uploader")
-
-    if uploaded_file is not None:
-        file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type, "FileSize": uploaded_file.size}
-        st.write(file_details)
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-
-        if st.button("Detect Now"):
-            prediction = predict_deepfake(image)
-            if prediction < 0.5:
-                st.write("Fake Image")
-            else:
-                st.write("Real Image")
-    else:
-        st.warning("Please upload an image.")
-
-if __name__ == "__main__":
-    show_home()