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New Text Document.txt

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+torch
+transformers
+gradio

app.py

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+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import gradio as gr
+
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
+model.load_state_dict(torch.load('best_model (4).pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Prediction function
+def classify_news(text):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        predicted_class = torch.argmax(probs, dim=1).item()
+        labels = ["Fake", "True"]
+        return {labels[0]: float(probs[0][0]), labels[1]: float(probs[0][1])}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_news,
+    inputs=gr.Textbox(lines=10, placeholder="Paste a news article here..."),
+    outputs=gr.Label(num_top_classes=2),
+    title="Fake News Detector",
+    description="Detect whether a news article is real or fake using a fine-tuned DistilBERT model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

best_model (4).pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c9e22fd8205fc26134360716442c61654b44bc4c063af46a124d3a2c368140be
+size 267861862