Create app.py

app.py

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+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import gradio as gr
+import matplotlib.pyplot as plt
+
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+def predict_fake_news(text):
+    input_ids = tokenizer.encode(text, return_tensors='pt')
+    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    fake_confidence = 1 if "fake" in generated_text.lower() else 0
+    real_confidence = 1 - fake_confidence
+    
+    fig, ax = plt.subplots()
+    ax.bar(["Real", "Fake"], [real_confidence, fake_confidence], color=['blue', 'red'])
+    plt.ylim(0, 1)
+    plt.xticks(rotation=45)
+    plt.title("Prediction Confidence")
+    
+    return fig
+
+input_text = gr.Textbox(lines=7, label="Paste the news article here", placeholder="Example: Scientists have discovered a new cure for cancer.")
+output_graph = gr.Image(label="Prediction Confidence")
+
+examples = [["New study shows coffee may prevent heart disease.", "Real"],
+            ["Aliens have landed in New York City!", "Fake"],
+            ["Global warming effects becoming more severe.", "Real"]]
+
+gr.Interface(predict_fake_news, inputs=input_text, outputs=output_graph, title="Real/Fake News Detector", theme="soft", examples=examples).launch()