Update app.py

app.py

@@ -1,43 +1,16 @@
-from transformers import pipeline
 import gradio as gr
-from PIL import Image
-import torch
-import os
-import cv2
-import random
+from transformers import pipeline
 
-# Load text classification model
-news_classifier = pipeline(
-    "text-classification",
-    model="jy46604790/Fake-News-Bert-Detect",
-    tokenizer="jy46604790/Fake-News-Bert-Detect"
-)
+news_classifier = pipeline("text-classification", model="typeform/distilbert-base-uncased-mnli")
 
 def classify_text(text):
     res = news_classifier(text[:512])[0]
-    label = res["label"]
-    score = round(res["score"] * 100, 2)
-    return f"📰 {label} ({score}%)"
-
-def detect_fake_image(img):
-    return "🛑 Image analysis coming soon."
-
-def analyze_video(video_file):
-    return "🎥 Video frame analysis coming soon."
+    return f"{res['label']} ({round(res['score'] * 100, 2)}%)"
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🔍 Fake News & Deepfake Checker (Beta)")
-    with gr.Tab("Text"):
-        t_input = gr.Textbox(lines=4, placeholder="Enter text or news here...")
-        t_output = gr.Textbox()
-        gr.Button("Check Text").click(classify_text, t_input, t_output)
-    with gr.Tab("Image"):
-        i_input = gr.Image(type="pil")
-        i_output = gr.Textbox()
-        gr.Button("Check Image").click(detect_fake_image, i_input, i_output)
-    with gr.Tab("Video"):
-        v_input = gr.Video()
-        v_output = gr.Textbox()
-        gr.Button("Check Video").click(analyze_video, v_input, v_output)
+    gr.Markdown("## 🧠 Fake News Detector (Text Only)")
+    text_input = gr.Textbox(label="Paste your news here")
+    output = gr.Textbox(label="Prediction")
+    gr.Button("Check News").click(classify_text, text_input, output)
 
 demo.launch()