Update app.py

app.py

@@ -6,6 +6,9 @@ import torchaudio
 import torchvision
 import tensorflow as tf
 from transformers import pipeline
+from PIL import Image
+import requests
+from io import BytesIO
 
 # Load a fake news detection model from Hugging Face
 fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
@@ -14,70 +17,77 @@ fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-fi
 st.set_page_config(page_title="Fake News Detector", layout="wide")
 st.title("📰 Fake News Detector")
 
-# Function to fetch real news links from various open sources
-def fetch_real_news_links():
-    return [
-        "https://www.bbc.com/news",
-        "https://www.cnn.com",
-        "https://www.reuters.com",
-        "https://huggingface.co/datasets/misinformation",
-        "https://www.wildfire.ai/deepfake-news-dataset",
-        "https://www.snopes.com",
-        "https://www.factcheck.org"
-    ]
-
-# Input Section
-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)
-    analyze_text = st.button("Analyze Text")
+# Tabs for Input and Results
+tab1, tab2 = st.tabs(["Input", "Results"])
 
-elif option == "Image":
-    uploaded_file = st.file_uploader("Upload an image of a news article", type=["jpg", "png", "jpeg"])
-    analyze_image = st.button("Analyze Image")
-
-elif option == "Video Link":
-    video_url = st.text_input("Enter a video news link to check")
-    analyze_video = st.button("Analyze Video")
-
-# Results Section
-if "analyze" not in st.session_state:
-    st.session_state["analyze"] = False
+# 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
 
-if (option == "Text" and analyze_text and news_text.strip()) or \
-   (option == "Image" and analyze_image and uploaded_file) or \
-   (option == "Video Link" and analyze_video and video_url.strip()):
-    st.session_state["analyze"] = True
-    st.session_state["news_text"] = news_text if option == "Text" else ""
-    st.session_state["uploaded_file"] = uploaded_file if option == "Image" else None
-    st.session_state["video_url"] = video_url if option == "Video Link" else ""
-    st.rerun()
+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"):
+            if not news_text.strip():
+                st.warning("Please enter some text.")
+            else:
+                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!")
+    
+    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!")
 
-if st.session_state["analyze"]:
-    with st.spinner("Analyzing..."):
+with tab2:
+    if st.session_state.get("analyze", False):
         news_text = st.session_state.get("news_text", "")
-        hf_result = "uncertain"
-        
-        if news_text:
+        with st.spinner("Analyzing..."):
+            # Check using Hugging Face model
             hf_result = fake_news_pipeline(news_text)[0]['label'].lower()
-        
-        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."
-        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 = "There is uncertainty in the classification. Further verification is recommended."
-        
-        # Conclusion Section
-        st.subheader("📌 Conclusion")
-        st.write(conclusion)
-        
-        # Display real news sources
-        st.subheader("🔗 Reliable News Sources")
-        for link in fetch_real_news_links():
-            st.markdown(f"[🔗 {link}]({link})")
+            
+            # 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])
+            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 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})")