initial commit

app.py

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+import gradio as gr
+import lightgbm as lgb
+import joblib
+
+# Load your trained model (assuming it's saved as 'lgbm_model.pkl')
+model = joblib.load('lgbm_model.pkl')
+
+def classify_text(text):
+    # Convert the input text to the appropriate format for your model
+    # For simplicity, let's assume you have a function `preprocess_text` for this
+    # processed_text = preprocess_text(text)
+    
+    # If your model expects numerical features, convert text to numerical features
+    # For example:
+    # features = text_to_features(processed_text)
+    
+    # Here, we assume the model can take raw text directly for simplicity
+    prediction = model.predict([text])
+    
+    return int(prediction[0])
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs=gr.outputs.Label(num_top_classes=1),
+    title="Fake News Classifier",
+    description="Enter text to classify if it's fake (1) or not fake (0).",
+    examples=["This is a sample news article."]
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+lightgbm