Update app.py

app.py

@@ -9,14 +9,30 @@ import cv2
 import numpy as np
 from bs4 import BeautifulSoup
 
-# Load Fake News Detection Model
-fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+# 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")
 
 def classify_text(news_text):
-    result = fake_news_pipeline(news_text)[0]
-    label = result['label'].lower()
-    score = result['score'] * 100
-    return ("Fake" if label == "fake" else "Real"), round(score, 2)
+    result1 = fake_news_pipeline_1(news_text)[0]
+    result2 = fake_news_pipeline_2(news_text)[0]
+    result3 = fake_news_pipeline_3(news_text)[0]
+    
+    scores = {
+        "Fake": 0,
+        "Real": 0
+    }
+    
+    for result in [result1, result2, result3]:
+        label = result['label'].lower()
+        score = result['score'] * 100
+        scores["Fake" if "fake" in label else "Real"] += score
+    
+    final_label = "Fake" if scores["Fake"] > scores["Real"] else "Real"
+    final_score = round(max(scores.values()) / 3, 2)
+    
+    return final_label, final_score
 
 def analyze_image(image):
     try:
@@ -75,7 +91,6 @@ with col1:
         else:
             st.warning("Please enter some text.")
 
-
 with col2:
     st.header("Image News Analysis")
     uploaded_image = st.file_uploader("Upload a news image", type=["jpg", "png", "jpeg"])