app.py

# import streamlit as st
# import numpy as np
# import cv2
# import tempfile
# import os

# # ---- Page Configuration ----
# st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide")

# st.title("📰 Fake News & Deepfake Detection Tool")
# st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI")

# # ---- Fake News Detection Section ----
# st.subheader("📝 Fake News Detection")
# news_input = st.text_area("Enter News Text:", "Type here...")

# if st.button("Check News"):
#     st.write("🔍 Processing...")
#     # Fake news detection logic (Placeholder)
#     st.success("✅ Result: This news is FAKE.")  # Replace with ML Model

# # ---- Deepfake Image Detection Section ----
# st.subheader("📸 Deepfake Image Detection")
# uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])

# if uploaded_image is not None:
#     st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
#     if st.button("Analyze Image"):
#         st.write("🔍 Processing...")
#         # Deepfake detection logic (Placeholder)
#         st.error("⚠️ Result: This image is a Deepfake.")  # Replace with model

# # ---- Deepfake Video Detection Section ----
# st.subheader("🎥 Deepfake Video Detection")
# uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])

# if uploaded_video is not None:
#     st.video(uploaded_video)
#     if st.button("Analyze Video"):
#         st.write("🔍 Processing...")
#         # Deepfake video detection logic (Placeholder)
#         st.warning("⚠️ Result: This video contains Deepfake elements.")  # Replace with model

# st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")


import streamlit as st
import cv2
import numpy as np
import tempfile
import os
from PIL import Image

def compress_image(image, quality=20):
    img = Image.open(image)
    img = img.convert("RGB")
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
    img.save(temp_file.name, "JPEG", quality=quality)
    return temp_file.name

def compress_video(video):
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
    cap = cv2.VideoCapture(video)
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    frame_width = int(cap.get(3) // 2)
    frame_height = int(cap.get(4) // 2)
    out = cv2.VideoWriter(temp_file.name, fourcc, 20.0, (frame_width, frame_height))
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame = cv2.resize(frame, (frame_width, frame_height))
        out.write(frame)
    
    cap.release()
    out.release()
    return temp_file.name

st.title("🕵️‍♂️ Fake News & Deepfake Detection Tool")

st.sidebar.header("Upload your file")
option = st.sidebar.radio("Select file type", ["Image", "Video", "Text"])

if option == "Image":
    uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        compressed_path = compress_image(uploaded_file)
        image = Image.open(compressed_path)
        st.image(image, caption="Compressed Image", use_column_width=True)
        st.success("✅ Image uploaded and compressed successfully!")

elif option == "Video":
    uploaded_file = st.sidebar.file_uploader("Upload a video", type=["mp4", "avi", "mov"])
    if uploaded_file is not None:
        compressed_path = compress_video(uploaded_file)
        st.video(compressed_path)
        st.success("✅ Video uploaded and compressed successfully!")

elif option == "Text":
    text_input = st.text_area("Enter your text for analysis")
    if text_input:
        st.write("🔍 Fake news detection processing...")
        st.success("✅ Text analysis completed!")