File size: 2,953 Bytes
39abc8d 56e3c29 97e3cb7 39abc8d dbd5cd3 51b55ad 97e3cb7 51b55ad 97e3cb7 51b55ad 97e3cb7 51b55ad 97e3cb7 39abc8d 97e3cb7 39abc8d 62aecb5 dbd5cd3 7f38c40 dbd5cd3 97e3cb7 dbd5cd3 97e3cb7 39abc8d 97e3cb7 39abc8d dbd5cd3 7f38c40 62aecb5 dbd5cd3 97e3cb7 dbd5cd3 39abc8d 62aecb5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import streamlit as st
import numpy as np
import cv2
import tempfile
import os
from PIL import Image
# ---- 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...")
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"])
def compress_image(image, quality=50, max_size=(100, 100)): # ✅ Better quality & small size
img = Image.open(image).convert("RGB")
img.thumbnail(max_size)
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
img.save(temp_file.name, "JPEG", quality=quality)
return temp_file.name
if uploaded_image is not None:
compressed_image_path = compress_image(uploaded_image)
st.image(compressed_image_path, caption="🖼️ Compressed Image", use_column_width=True)
if st.button("Analyze Image"):
st.write("🔍 Processing...")
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"])
def compress_video(video):
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
temp_video.write(video.read())
video_path = temp_video.name
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
st.error("❌ Error: Unable to read video!")
return None
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
# ✅ Further Reduce Video Resolution
frame_width = 200
frame_height = 120
out = cv2.VideoWriter(temp_file.name, fourcc, 12.0, (frame_width, frame_height)) # ✅ Lower FPS to 12
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
if uploaded_video is not None:
compressed_video_path = compress_video(uploaded_video)
if compressed_video_path:
st.video(compressed_video_path)
if st.button("Analyze Video"):
st.write("🔍 Processing...")
st.warning("⚠️ Result: This video contains Deepfake elements.") # Replace with model
st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**") |