Update app.py

app.py

@@ -2,95 +2,75 @@ import streamlit as st
 import requests
 from transformers import pipeline
 from PIL import Image
-import torch
 import torchvision.transforms as transforms
 
-# Load Fake News Detection Model from Hugging Face
-fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+# Load a more accurate fake news detection model
+fake_news_pipeline = pipeline("text-classification", model="roberta-base-openai-detector")
 
+# Function to classify news text
 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 verify news with Google Fact Check API
+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 (Dummy function for now)
 def analyze_image(image):
     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")
 
-# Dividing Input Section
+# Sidebar Navigation
+st.sidebar.title("Select Input Type")
+option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])
+
+# Input Sections
 col1, col2, col3 = st.columns(3)
 
-# Text Input Section
+# Text Analysis Section
 with col1:
-    st.subheader("📄 Enter Text")
-    news_text = st.text_area("Enter the news content to check:", height=150)
-    analyze_text_clicked = st.button("Analyze News")
-    if analyze_text_clicked:
+    st.subheader("📄 Text News Check")
+    news_text = st.text_area("Enter the news content:", height=200)
+    if st.button("Analyze News"):
         if not news_text.strip():
             st.warning("Please enter some text.")
         else:
             result, accuracy = classify_text(news_text)
-            st.session_state["news_text"] = news_text
-            st.session_state["result_text"] = result
-            st.session_state["accuracy_text"] = accuracy
+            verification_link = verify_news(news_text)
+
+            if result == "Fake":
+                st.error(f"❌ Likely **Fake News**! (Confidence: {accuracy}%)")
+            else:
+                st.success(f"✅ Likely **Real News**! (Confidence: {accuracy}%)")
+
+            st.markdown(f"[🔎 Verify on Google Fact Check]({verification_link})")
 
 # Image Upload Section
 with col2:
-    st.subheader("🖼️ Upload Image")
+    st.subheader("🖼️ Image News Check")
     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)
-        st.session_state["news_image"] = image
         st.image(image, caption="Uploaded Image", use_column_width=True)
         st.info(analyze_image(image))
 
-# Video Link Input Section
+# Video Link Section
 with col3:
-    st.subheader("🎥 Enter Video Link")
+    st.subheader("🎥 Video News Check")
     video_url = st.text_input("Enter the video link:")
-    analyze_video_clicked = st.button("Analyze Video")
-    if analyze_video_clicked:
+    if st.button("Analyze Video"):
         if not video_url.strip():
             st.warning("Please enter a valid video link.")
         else:
-            st.session_state["video_url"] = video_url
             st.video(video_url)
             st.info("Video analysis feature coming soon!")
 
-# Results Section
-st.subheader("📊 Analysis Results")
-if "result_text" in st.session_state:
-    result = st.session_state.get("result_text")
-    accuracy = st.session_state.get("accuracy_text")
-    verification_link = verify_news(st.session_state.get("news_text"))
-    
-    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",
-        "https://deepfake-o-meter.ai",
-        "https://huggingface.co/models?pipeline_tag=text-classification"
-    ]
-    for link in sources:
-        st.markdown(f"[🔗 {link}]({link})")
-    st.markdown(f"[🔎 Verify on Google]({verification_link})")