Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import os
-import cv2
 import torch
 import torchaudio
 import torchvision
@@ -8,7 +7,7 @@ import tensorflow as tf
 from transformers import pipeline
 from PIL import Image
 import requests
-from io import BytesIO
+import io
 
 # Load a fake news detection model from Hugging Face
 fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
@@ -20,21 +19,16 @@ st.title("📰 Fake News Detector")
 # Tabs for Input and Results
 tab1, tab2 = st.tabs(["Input", "Results"])
 
-# Function to fetch real news links based on keywords
-def fetch_real_news_links(query):
-    search_urls = [
-        f"https://www.bbc.co.uk/search?q={query}",
-        f"https://www.cnn.com/search?q={query}",
-        f"https://www.reuters.com/search/news?blob={query}",
-        f"https://www.snopes.com/?s={query}",
-        f"https://www.factcheck.org/search/?q={query}"
-    ]
-    return search_urls
+# Function to fetch real news links based on content
+def fetch_real_news_links(news_text):
+    query = news_text.replace(" ", "+")
+    search_url = f"https://www.google.com/search?q={query}+site:bbc.com+OR+site:cnn.com+OR+site:reuters.com"
+    return search_url
 
 with tab1:
     st.sidebar.title("Select Input Type")
     option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])
-    
+
     if option == "Text":
         news_text = st.text_area("Enter the news content to check:", height=200)
         if st.button("Analyze News"):
@@ -44,23 +38,21 @@ with tab1:
                 st.session_state["news_text"] = news_text
                 st.session_state["analyze"] = True
                 st.rerun()
-    
+
     elif option == "Image":
         uploaded_file = st.file_uploader("Upload an image of a news article", type=["jpg", "png", "jpeg"])
         if uploaded_file is not None:
             image = Image.open(uploaded_file)
             st.image(image, caption="Uploaded Image", use_column_width=True)
-            st.session_state["image_uploaded"] = True
-            st.warning("⚠️ Image analysis is coming soon!")
-    
+            st.info("🔍 Text extraction from images is under development.")
+
     elif option == "Video Link":
         video_url = st.text_input("Enter a video news link to check")
         if st.button("Analyze Video"):
             if not video_url.strip():
                 st.warning("Please enter a valid URL.")
             else:
-                st.session_state["video_url"] = video_url
-                st.warning("⚠️ Video analysis is coming soon!")
+                st.info("🔍 Video analysis is under development.")
 
 with tab2:
     if st.session_state.get("analyze", False):
@@ -69,25 +61,21 @@ with tab2:
             # Check using Hugging Face model
             hf_result = fake_news_pipeline(news_text)[0]['label'].lower()
             
-            # Display result
             if hf_result == "fake":
                 st.error("❌ This news is likely **Fake**!", icon="⚠️")
-                conclusion = "The analysis suggests that this news might be fabricated or misleading. Please verify from credible sources."
-                real_news_links = fetch_real_news_links(news_text[:50])
+                conclusion = "The analysis suggests that this news might be fabricated or misleading."
             elif hf_result == "real":
                 st.success("✅ This news is likely **Real**!", icon="✅")
                 conclusion = "The analysis indicates that this news appears to be credible and factual."
-                real_news_links = fetch_real_news_links(news_text[:50])
             else:
                 st.info("🤔 The result is uncertain. Please verify from trusted sources.")
-                conclusion = "There is uncertainty in the classification. Further verification is recommended."
-                real_news_links = []
+                conclusion = "Further verification is recommended."
             
             # Conclusion Section
             st.subheader("📌 Conclusion")
             st.write(conclusion)
             
             # Display real news sources
-            st.subheader("🔗 Related News Articles")
-            for link in real_news_links:
-                st.markdown(f"[🔗 {link}]({link})")
+            st.subheader("🔗 Related News Sources")
+            real_news_link = fetch_real_news_links(news_text)
+            st.markdown(f"[🔗 Click here to verify]({real_news_link})")