Create app.py

app.py

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+from transformers import pipeline
+from PIL import Image
+import gradio as gr
+
+# Load models
+text_model = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
+image_model = pipeline("image-classification", model="prithivMLmods/Deep-Fake-Detector-v2-Model")
+
+# Text detection function
+def detect_text_misinformation(text):
+    if not text.strip():
+        return "Please enter some text.", None
+    result = text_model(text)[0]
+    return f"Prediction: {result['label']}", f"Confidence: {result['score']:.2f}"
+
+# Image detection function
+def detect_image_deepfake(image):
+    result = image_model(image)[0]
+    label_map = {
+        "LABEL_0": ("Unauthentic", "❌"),
+        "LABEL_1": ("Authentic", "✅")
+    }
+    label, icon = label_map.get(result['label'].upper(), (result['label'], "❓"))
+    return f"{icon} Prediction: {label}", f"Confidence: {result['score']:.2f}"
+
+# Gradio Interface
+text_interface = gr.Interface(
+    fn=detect_text_misinformation,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a news statement or claim..."),
+    outputs=["text", "text"],
+    title="📰 Misinformation Detection"
+)
+
+image_interface = gr.Interface(
+    fn=detect_image_deepfake,
+    inputs=gr.Image(type="pil"),
+    outputs=["text", "text"],
+    title="🖼️ Deepfake Image Detection"
+)
+
+demo = gr.TabbedInterface([text_interface, image_interface], ["Text Misinformation", "Image Deepfake"])
+demo.launch()