Update app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the AI detection model pipeline from Hugging Face
+# We're using a well-regarded RoBERTa-based model for this task.
+pipe = pipeline("text-classification", model="michellejieli/roberta-base-openai-detector")
+
+def detect_ai_text(text):
+    """
+    Analyzes the input text and returns the model's prediction.
+    The model returns a list of dictionaries. We want the one that tells us the 'AI' score.
+    """
+    results = pipe(text)
+    # The model outputs probabilities for both 'LABEL_0' (Human) and 'LABEL_1' (AI).
+    # We'll return the full results for clarity.
+    return {item['label']: item['score'] for item in results}
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=detect_ai_text,
+    inputs=gr.Textbox(lines=10, placeholder="Paste the text you want to analyze here..."),
+    outputs="json",
+    title="AI Content Detector",
+    description="A simple API to detect AI-generated text. Powered by roberta-base-openai-detector."
+)
+
+# Launch the app. The `share=True` argument is what makes the API accessible.
+iface.launch()