Update app.py

app.py

@@ -1,17 +1,28 @@
 import streamlit as st
-import os
-import torch
-import torchaudio
-import torchvision
-import tensorflow as tf
+import requests
 from transformers import pipeline
 from PIL import Image
-import requests
+import torch
+import torchvision.transforms as transforms
 import io
 
-# Load a fake news detection model from Hugging Face
+# Load Fake News Detection Model from Hugging Face
 fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
 
+def classify_text(news_text):
+    result = fake_news_pipeline(news_text)[0]['label'].lower()
+    return "Fake" if result == "fake" else "Real"
+
+def analyze_image(image):
+    # Convert image to tensor (Placeholder: Model should be updated with a real image classifier)
+    transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
+    image_tensor = transform(image).unsqueeze(0)
+    return "Image analysis feature coming soon!"
+
+def verify_news(news_text):
+    search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
+    return search_url
+
 # Streamlit UI
 st.set_page_config(page_title="Fake News Detector", layout="wide")
 st.title("📰 Fake News Detector")
@@ -19,12 +30,6 @@ st.title("📰 Fake News Detector")
 # Tabs for Input and Results
 tab1, tab2 = st.tabs(["Input", "Results"])
 
-# 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"])
@@ -36,46 +41,51 @@ with tab1:
                 st.warning("Please enter some text.")
             else:
                 st.session_state["news_text"] = news_text
-                st.session_state["analyze"] = True
-                st.rerun()
+                st.session_state["analyze_text"] = True
+                st.experimental_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.info("🔍 Text extraction from images is under development.")
+        uploaded_image = st.file_uploader("Upload a news image", type=["jpg", "png", "jpeg"])
+        if uploaded_image and st.button("Analyze Image"):
+            image = Image.open(uploaded_image)
+            st.session_state["news_image"] = image
+            st.session_state["analyze_image"] = True
+            st.experimental_rerun()
 
     elif option == "Video Link":
-        video_url = st.text_input("Enter a video news link to check")
+        video_url = st.text_input("Enter the video link:")
         if st.button("Analyze Video"):
             if not video_url.strip():
-                st.warning("Please enter a valid URL.")
+                st.warning("Please enter a valid video link.")
             else:
-                st.info("🔍 Video analysis is under development.")
+                st.session_state["video_url"] = video_url
+                st.session_state["analyze_video"] = True
+                st.experimental_rerun()
 
 with tab2:
-    if st.session_state.get("analyze", False):
+    if st.session_state.get("analyze_text", False):
         news_text = st.session_state.get("news_text", "")
-        with st.spinner("Analyzing..."):
-            # Check using Hugging Face model
-            hf_result = fake_news_pipeline(news_text)[0]['label'].lower()
+        with st.spinner("Analyzing text..."):
+            result = classify_text(news_text)
+            verification_link = verify_news(news_text)
             
-            if hf_result == "fake":
+            if result == "Fake":
                 st.error("❌ This news is likely **Fake**!", icon="⚠️")
-                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."
             else:
-                st.info("🤔 The result is uncertain. Please verify from trusted sources.")
-                conclusion = "Further verification is recommended."
-            
-            # Conclusion Section
-            st.subheader("📌 Conclusion")
-            st.write(conclusion)
+                st.success("✅ This news is likely **Real**!", icon="✅")
             
-            # Display real news sources
-            st.subheader("🔗 Related News Sources")
-            real_news_link = fetch_real_news_links(news_text)
-            st.markdown(f"[🔗 Click here to verify]({real_news_link})")
+            st.subheader("🔍 Verification & Trusted Sources")
+            sources = ["https://www.bbc.com/news", "https://www.cnn.com", "https://www.reuters.com"]
+            for link in sources:
+                st.markdown(f"[🔗 {link}]({link})")
+            st.markdown(f"[🔎 Verify on Google]({verification_link})")
+
+    if st.session_state.get("analyze_image", False):
+        image = st.session_state.get("news_image")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.info(analyze_image(image))
+
+    if st.session_state.get("analyze_video", False):
+        video_url = st.session_state.get("video_url", "")
+        st.video(video_url)
+        st.info("Video analysis feature coming soon!")