File size: 3,391 Bytes
56e3c29 c29b197 56e3c29 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
# 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()
|