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# 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!")
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