Update app.py

app.py

@@ -2,10 +2,11 @@ import streamlit as st
 import requests
 from transformers import pipeline
 from PIL import Image
-import torchvision.transforms as transforms
-import torch
-import cv2
 import numpy as np
+import cv2
+import torch
+import torchvision.transforms as transforms
+from deepface import DeepFace
 
 # Load Fake News Detection Model
 fake_news_pipeline = pipeline("text-classification", model="roberta-base-openai-detector")
@@ -22,23 +23,13 @@ def verify_news(news_text):
     search_url = f"https://toolbox.google.com/factcheck/explorer/search/{'+'.join(news_text.split())}"
     return search_url
 
-# Function to analyze images for fake news
+# Function to analyze deepfake images
 def analyze_image(image):
-    transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
-    image_tensor = transform(image).unsqueeze(0)
-
-    # Convert to OpenCV format
-    image_cv = np.array(image)
-    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
-
-    # Apply Fake Image Detection (Basic Example)
-    edges = cv2.Canny(image_cv, 100, 200)
-    edge_percentage = np.sum(edges) / (image_cv.shape[0] * image_cv.shape[1])
-
-    if edge_percentage > 0.1:
-        return "❌ This image might be **manipulated** or **deepfake**!"
-    else:
-        return "✅ This image appears to be **authentic**."
+    try:
+        result = DeepFace.analyze(np.array(image), actions=['age', 'gender', 'race', 'emotion'])
+        return f"✅ This image appears **real**! AI Analysis: {result}"
+    except Exception:
+        return "❌ This image might be **deepfake or manipulated**!"
 
 # Function to analyze video metadata
 def analyze_video(video_url):
@@ -48,7 +39,8 @@ def analyze_video(video_url):
     if "title" in response:
         title = response["title"]
         source = response["provider_name"]
-        return f"🎥 Video Title: {title}\n🔗 Source: {source}"
+        verification_link = verify_news(title)
+        return f"🎥 Video Title: {title}\n🔗 Source: {source}\n[🔎 Verify this Video]({verification_link})"
     else:
         return "❌ Unable to fetch video details! Check if the link is correct."