Update app.py

app.py

@@ -1,150 +1,94 @@
 import streamlit as st
 import requests
 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 deepface import DeepFace
 from bs4 import BeautifulSoup
+import re
 
-# Load Fake News Detection Model (Text)
+# Load Fake News Detection Model
 fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
 
-# Function to classify text as Fake or Real
 def classify_text(news_text):
     result = fake_news_pipeline(news_text)[0]
     label = result['label'].lower()
     score = result['score'] * 100  # Convert to percentage
     return ("Fake" if label == "fake" else "Real"), round(score, 2)
 
-# Function to analyze image authenticity
 def analyze_image(image):
     try:
-        image_array = np.array(image)
-        result = DeepFace.analyze(image_array, actions=["age", "gender", "race"], enforce_detection=False)
-        return "Real" if result else "Fake", 90  # Placeholder accuracy
-    except Exception as e:
-        return "Error", str(e)
+        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."
+
+def analyze_video(video_url):
+    return "Video analysis feature coming soon!"
 
-# Function to verify news from open sources
 def verify_news(news_text):
     search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
-    response = requests.get(search_url)
-    soup = BeautifulSoup(response.text, "html.parser")
-    
-    results = []
-    for link in soup.find_all("a", href=True):
-        if "http" in link["href"] and "google" not in link["href"]:
-            results.append((link.text.strip(), link["href"]))
-        if len(results) >= 3:  # Limit to 3 sources
-            break
-    return results
+    return search_url
 
-# Streamlit UI Configuration
+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 Input Selection
+# Sidebar for Input Selection
 st.sidebar.title("Select Input Type")
 option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])
 
-# Session Variables
-if "result_text" not in st.session_state:
-    st.session_state["result_text"] = None
-if "accuracy_text" not in st.session_state:
-    st.session_state["accuracy_text"] = None
-if "result_image" not in st.session_state:
-    st.session_state["result_image"] = None
-if "accuracy_image" not in st.session_state:
-    st.session_state["accuracy_image"] = None
-if "video_result" not in st.session_state:
-    st.session_state["video_result"] = None
-
 # 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:
-        if not news_text.strip():
-            st.warning("Please enter some text.")
-        else:
-            result, accuracy = classify_text(news_text)
-            st.session_state["result_text"] = result
-            st.session_state["accuracy_text"] = accuracy
-            verification_links = verify_news(news_text)
-            st.session_state["verification_text"] = verification_links
+    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)})")
 
 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:
         image = Image.open(uploaded_image)
-        result, accuracy = analyze_image(image)
-        st.session_state["result_image"] = result
-        st.session_state["accuracy_image"] = accuracy
+        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**!")
 
 elif option == "Video Link":
     video_url = st.text_input("Enter the video link:")
     analyze_video_clicked = st.button("Analyze Video")
-
-    if analyze_video_clicked:
-        if not video_url.strip():
-            st.warning("Please enter a valid video link.")
-        else:
-            st.session_state["video_result"] = "Real"  # Placeholder (Video verification requires advanced models)
-
-# Results Section
-st.subheader("📊 Analysis Results")
-
-# Text Results
-if st.session_state.get("result_text"):
-    result = st.session_state["result_text"]
-    accuracy = st.session_state["accuracy_text"]
-    st.subheader("📝 Text Analysis")
-    
-    if result == "Fake":
-        st.error(f"❌ This news is likely **Fake**! (Accuracy: {accuracy}%)", icon="⚠️")
-    else:
-        st.success(f"✅ This news is likely **Real**! (Accuracy: {accuracy}%)", icon="✅")
-
-    st.subheader("🔍 Verification & Trusted Sources")
-    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"
-    ]
-    for link in sources:
-        st.markdown(f"[🔗 {link}]({link})")
-
-    if "verification_text" in st.session_state:
-        for name, link in st.session_state["verification_text"]:
-            st.markdown(f"[🔍 {name}]({link})")
-
-# Image Results
-if st.session_state.get("result_image"):
-    result = st.session_state["result_image"]
-    accuracy = st.session_state["accuracy_image"]
-    st.subheader("🖼️ Image Analysis")
-    
-    if result == "Fake":
-        st.error(f"❌ This image is likely **Fake**! (Accuracy: {accuracy}%)", icon="⚠️")
-    else:
-        st.success(f"✅ This image is likely **Real**! (Accuracy: {accuracy}%)", icon="✅")
-
-# Video Results
-if st.session_state.get("video_result"):
-    result = st.session_state["video_result"]
-    st.subheader("📹 Video Analysis")
-    
-    if result == "Fake":
-        st.error("❌ This video is likely **Fake**!", icon="⚠️")
-    else:
-        st.success("✅ This video is likely **Real**!", icon="✅")
-
+    if analyze_video_clicked and video_url.strip():
+        st.video(video_url)
+        result = analyze_video(video_url)
+        st.info(result)