Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import requests
 import torch
-import torchvision.transforms as transforms
 from transformers import pipeline
 from deepface import DeepFace
 from PIL import Image
@@ -10,29 +9,25 @@ import numpy as np
 from bs4 import BeautifulSoup
 
 # Load Fake News Detection Models
-fake_news_pipeline_1 = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
-fake_news_pipeline_2 = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
-fake_news_pipeline_3 = pipeline("text-classification", model="roberta-base-openai-detector")
+fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+sentiment_pipeline = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+ai_detector_pipeline = pipeline("text-classification", model="roberta-base-openai-detector")
 
 def classify_text(news_text):
-    result1 = fake_news_pipeline_1(news_text)[0]
-    result2 = fake_news_pipeline_2(news_text)[0]
-    result3 = fake_news_pipeline_3(news_text)[0]
+    fake_news_result = fake_news_pipeline(news_text)[0]
+    sentiment_result = sentiment_pipeline(news_text)[0]
+    ai_result = ai_detector_pipeline(news_text)[0]
     
-    scores = {
-        "Fake": 0,
-        "Real": 0
-    }
+    fake_label = fake_news_result['label'].lower()
+    sentiment_label = sentiment_result['label'].lower()
+    ai_label = ai_result['label'].lower()
     
-    for result in [result1, result2, result3]:
-        label = result['label'].lower()
-        score = result['score'] * 100
-        scores["Fake" if "fake" in label else "Real"] += score
+    fake_score = fake_news_result['score'] * 100
+    sentiment_score = sentiment_result['score'] * 100
+    ai_score = ai_result['score'] * 100
     
-    final_label = "Fake" if scores["Fake"] > scores["Real"] else "Real"
-    final_score = round(max(scores.values()) / 3, 2)
-    
-    return final_label, final_score
+    final_label = "Fake" if fake_label == "fake" or ai_label == "ai-generated" else "Real"
+    return final_label, round(fake_score, 2), round(sentiment_score, 2), round(ai_score, 2)
 
 def analyze_image(image):
     try:
@@ -61,14 +56,14 @@ def analyze_video(video_path):
         return "Error: " + str(e)
 
 def verify_news(news_text):
-    search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
+    search_url = f"https://www.google.com/search?q={'%20'.join(news_text.split())}"
     sources = [
-        f"https://www.bbc.co.uk/search?q={'+'.join(news_text.split())}",
-        f"https://www.cnn.com/search?q={'+'.join(news_text.split())}",
-        f"https://www.reuters.com/search/news?blob={'+'.join(news_text.split())}",
-        f"https://www.factcheck.org/?s={'+'.join(news_text.split())}",
-        f"https://www.snopes.com/?s={'+'.join(news_text.split())}",
-        f"https://www.politifact.com/search/?q={'+'.join(news_text.split())}"
+        f"https://www.bbc.co.uk/search?q={'%20'.join(news_text.split())}",
+        f"https://www.cnn.com/search?q={'%20'.join(news_text.split())}",
+        f"https://www.reuters.com/search/news?blob={'%20'.join(news_text.split())}",
+        f"https://www.factcheck.org/?s={'%20'.join(news_text.split())}",
+        f"https://www.snopes.com/?s={'%20'.join(news_text.split())}",
+        f"https://www.politifact.com/search/?q={'%20'.join(news_text.split())}"
     ]
     return search_url, sources
 
@@ -82,9 +77,12 @@ with col1:
     news_text = st.text_area("Enter the news content to check:", height=200)
     if st.button("Analyze News", key="text_analyze"):
         if news_text.strip():
-            result, accuracy = classify_text(news_text)
+            result, fake_acc, sentiment_acc, ai_acc = classify_text(news_text)
             verification_link, sources = verify_news(news_text)
-            st.write(f"**Result:** {result} (Accuracy: {accuracy}%)")
+            st.write(f"**Result:** {result}")
+            st.write(f"**Fake News Score:** {fake_acc}%")
+            st.write(f"**Sentiment Score:** {sentiment_acc}%")
+            st.write(f"**AI Detection Score:** {ai_acc}%")
             st.markdown(f"[Verify on Google]({verification_link})")
             for source in sources:
                 st.markdown(f"[Check Source]({source})")