Update app.py

app.py

@@ -1,14 +1,13 @@
 import streamlit as st
 import requests
+import torch
+import torchvision.transforms as transforms
 from transformers import pipeline
 from deepface import DeepFace
 from PIL import Image
-import torch
-import torchvision.transforms as transforms
 import cv2
 import numpy as np
 from bs4 import BeautifulSoup
-import re
 
 # Load Fake News Detection Model
 fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
@@ -16,79 +15,74 @@ fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-fi
 def classify_text(news_text):
     result = fake_news_pipeline(news_text)[0]
     label = result['label'].lower()
-    score = result['score'] * 100  # Convert to percentage
+    score = result['score'] * 100
     return ("Fake" if label == "fake" else "Real"), round(score, 2)
 
 def analyze_image(image):
     try:
-        result = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
-        return "Fake" if result[0]['dominant_emotion'] in ['disgust', 'fear', 'sad'] else "Real"
-    except:
-        return "Error in analyzing image."
+        analysis = DeepFace.analyze(image, actions=["emotion"])
+        dominant_emotion = analysis[0]["dominant_emotion"]
+        return "Fake" if dominant_emotion in ["fear", "surprise"] else "Real"
+    except Exception as e:
+        return "Error: " + str(e)
 
-def analyze_video(video_url):
-    return "Video analysis feature coming soon!"
+def analyze_video(video_path):
+    try:
+        cap = cv2.VideoCapture(video_path)
+        frame_count = 0
+        results = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret or frame_count >= 10:
+                break
+            image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+            result = analyze_image(image)
+            results.append(result)
+            frame_count += 1
+        cap.release()
+        return "Fake" if results.count("Fake") > results.count("Real") else "Real"
+    except Exception as e:
+        return "Error: " + str(e)
 
 def verify_news(news_text):
     search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
     return search_url
 
-def scrape_verification_links(news_text):
-    sources = [
-        "https://www.bbc.com/news",
-        "https://www.cnn.com",
-        "https://www.reuters.com",
-        "https://factcheck.org",
-        "https://www.snopes.com",
-        "https://www.politifact.com"
-    ]
-    verification_links = {}
-    for source in sources:
-        try:
-            response = requests.get(source)
-            soup = BeautifulSoup(response.text, 'html.parser')
-            for link in soup.find_all('a', href=True):
-                if re.search(news_text[:5], link.text, re.IGNORECASE):
-                    verification_links[link.text] = source + link['href']
-        except:
-            continue
-    return verification_links
-
-# Streamlit UI
 st.set_page_config(page_title="Fake News Detector", layout="wide")
 st.title("📰 Fake News Detector")
 
-# Sidebar for Input Selection
 st.sidebar.title("Select Input Type")
 option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])
 
-# Input Section
 if option == "Text":
     news_text = st.text_area("Enter the news content to check:", height=200)
-    analyze_text_clicked = st.button("Analyze News")
-    if analyze_text_clicked and news_text.strip():
-        result, accuracy = classify_text(news_text)
-        verification_links = scrape_verification_links(news_text)
-        st.subheader("📊 Analysis Results")
-        st.error(f"❌ This news is likely **Fake**! (Accuracy: {accuracy}%)") if result == "Fake" else st.success(f"✅ This news is likely **Real**! (Accuracy: {accuracy}%)")
-        st.subheader("🔍 Verification & Trusted Sources")
-        for title, link in verification_links.items():
-            st.markdown(f"[🔗 {title}]({link})")
-        st.markdown(f"[🔎 Verify on Google]({verify_news(news_text)})")
+    if st.button("Analyze News"):
+        if news_text.strip():
+            result, accuracy = classify_text(news_text)
+            verification_link = verify_news(news_text)
+            st.write(f"**Result:** {result} (Accuracy: {accuracy}%)")
+            st.markdown(f"[Verify on Google]({verification_link})")
+        else:
+            st.warning("Please enter some text.")
 
 elif option == "Image":
     uploaded_image = st.file_uploader("Upload a news image", type=["jpg", "png", "jpeg"])
-    analyze_image_clicked = st.button("Analyze Image")
-    if uploaded_image and analyze_image_clicked:
+    if uploaded_image and st.button("Analyze Image"):
         image = Image.open(uploaded_image)
+        result = analyze_image(image)
         st.image(image, caption="Uploaded Image", use_column_width=True)
-        result = analyze_image(np.array(image))
-        st.error("❌ This image is likely **Fake**!") if result == "Fake" else st.success("✅ This image is likely **Real**!")
+        st.write(f"**Result:** {result}")
+        verification_link = verify_news("Fake news image verification")
+        st.markdown(f"[Verify on Google]({verification_link})")
 
 elif option == "Video Link":
     video_url = st.text_input("Enter the video link:")
-    analyze_video_clicked = st.button("Analyze Video")
-    if analyze_video_clicked and video_url.strip():
-        st.video(video_url)
-        result = analyze_video(video_url)
-        st.info(result)
+    if st.button("Analyze Video"):
+        if video_url.strip():
+            result = analyze_video(video_url)
+            st.video(video_url)
+            st.write(f"**Result:** {result}")
+            verification_link = verify_news("Fake news video verification")
+            st.markdown(f"[Verify on Google]({verification_link})")
+        else:
+            st.warning("Please enter a valid video link.")