| # 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 gradio as gr | |
| import cv2 | |
| import numpy as np | |
| # ✅ Image Size Limit (MB) | |
| MAX_IMAGE_SIZE_MB = 5 # 5MB se zyada allow nahi karega | |
| # ✅ Video Size Limit (MB) | |
| MAX_VIDEO_SIZE_MB = 20 # 20MB se zyada allow nahi karega | |
| # ✅ Image Processing Function | |
| def process_image(image): | |
| if image is None: | |
| return "❌ No image uploaded!" | |
| # ✅ Image Resize (Optional: 512x512) | |
| image = cv2.resize(image, (512, 512)) | |
| return image | |
| # ✅ Video Processing Function | |
| def process_video(video_path): | |
| if video_path is None: | |
| return "❌ No video uploaded!" | |
| # ✅ Video Size Check | |
| import os | |
| file_size_mb = os.path.getsize(video_path) / (1024 * 1024) | |
| if file_size_mb > MAX_VIDEO_SIZE_MB: | |
| return f"❌ Video is too large! (Size: {file_size_mb:.2f}MB) - Limit: {MAX_VIDEO_SIZE_MB}MB" | |
| return f"✅ Video uploaded successfully! (Size: {file_size_mb:.2f}MB)" | |
| # ✅ Gradio Interface | |
| with gr.Blocks() as app: | |
| gr.Markdown("## 🕵️♂️ Fake News & Deepfake Detection Tool") | |
| with gr.Row(): | |
| img_input = gr.Image(type="numpy", label="🖼 Upload Image (Max: 5MB)") | |
| img_output = gr.Image(label="📌 Processed Image") | |
| img_button = gr.Button("🔍 Detect Image") | |
| img_button.click(process_image, inputs=img_input, outputs=img_output) | |
| with gr.Row(): | |
| video_input = gr.File(label="🎥 Upload Video (Max: 20MB)", file_types=[".mp4"]) | |
| video_output = gr.Textbox(label="📌 Video Status") | |
| video_button = gr.Button("🔍 Detect Video") | |
| video_button.click(process_video, inputs=video_input, outputs=video_output) | |
| app.launch() | |