Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load the trained classifier and TF-IDF vectorizer
+try:
+    svm_clf = pickle.load(open('clf.pkl', 'rb'))
+    svm_tfidf = pickle.load(open('tfidf.pkl', 'rb'))
+except Exception as e:
+    raise Exception(f"Error loading model files: {str(e)}")
+
+def predict_text(text):
+    # Transform the input text using the TF-IDF vectorizer
+    text_transformed = svm_tfidf.transform([text])
+
+    # Get prediction (0.0 for human, 1.0 for AI)
+    prediction = svm_clf.predict(text_transformed)[0]
+
+    # Get decision score (distance from hyperplane)
+    decision_score = svm_clf.decision_function(text_transformed)[0]
+
+    # Sigmoid scaling to get a confidence-like score (0-100%)
+    ai_confidence = 100 * (1 / (1 + np.exp(-decision_score)))
+    human_confidence = 100 - ai_confidence
+
+    # Return formatted result
+    label = "AI" if prediction == 1.0 else "Human"
+    return f"Prediction: {label}\nConfidence: {ai_confidence:.2f}% AI, {human_confidence:.2f}% Human"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."),
+    outputs="text",
+    title="AI vs Human Text Classifier",
+    description="Enter text to classify it as AI-generated or Human-written, with confidence scores."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()