Update app.py

app.py

@@ -1,95 +1,150 @@
 import streamlit as st
 import requests
 from transformers import pipeline
-from deepface import DeepFace
 from PIL import Image
-import cv2
 import torch
 import torchvision.transforms as transforms
+import cv2
 import numpy as np
-import os
-import json
+from deepface import DeepFace
+from bs4 import BeautifulSoup
 
-def load_text_model():
-    return pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+# Load Fake News Detection Model (Text)
+fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
 
-def classify_text(news_text, text_model):
-    result = text_model(news_text)[0]
+# 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
+    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)
+
+# Function to verify news from open sources
 def verify_news(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"
-    ]
     search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
-    return sources, search_url
-
-def analyze_image(image_path):
-    try:
-        result = DeepFace.analyze(image_path, actions=['age', 'gender', 'emotion'])
-        return "Real", 85.0 if result else "Fake", 60.0
-    except:
-        return "Fake", 50.0
-
-def analyze_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    frames = []
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
+    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
-        frames.append(frame)
-    cap.release()
-    return "Real", 80.0 if len(frames) > 10 else "Fake", 40.0
+    return results
 
+# Streamlit UI Configuration
 st.set_page_config(page_title="Fake News Detector", layout="wide")
 st.title("📰 Fake News Detector")
 
-text_model = load_text_model()
-
+# Sidebar Input Selection
 st.sidebar.title("Select Input Type")
-option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video"])
+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:")
-    if st.button("Analyze News"):
-        if news_text.strip():
-            result, accuracy = classify_text(news_text, text_model)
-            sources, verification_link = verify_news(news_text)
-            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
-            for link in sources:
-                st.markdown(f"[🔗 {link}]({link})")
-            st.markdown(f"[🔎 Verify on Google]({verification_link})")
-        else:
+    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
 
 elif option == "Image":
-    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
-    if uploaded_image:
-        img = Image.open(uploaded_image)
-        img_path = "uploaded_image.jpg"
-        img.save(img_path)
-        if st.button("Analyze Image"):
-            result, accuracy = analyze_image(img_path)
-            st.image(img, caption="Uploaded Image", use_column_width=True)
-            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
-            os.remove(img_path)
-
-elif option == "Video":
-    uploaded_video = st.file_uploader("Upload a video", type=["mp4", "avi", "mov"])
-    if uploaded_video:
-        video_path = "uploaded_video.mp4"
-        with open(video_path, "wb") as f:
-            f.write(uploaded_video.read())
-        if st.button("Analyze Video"):
-            result, accuracy = analyze_video(video_path)
-            st.video(video_path)
-            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
-            os.remove(video_path)
+    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
+
+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="✅")
+