Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# 1. Load a text-generation pipeline from Hugging Face
+# (Choose any model from https://huggingface.co/models)
+generator = pipeline(
+    "text-generation",
+    model="bigscience/bloom-560m",  # Example smaller Bloom model
+    max_length=100
+)
+
+def chatbot(prompt):
+    """
+    Generate a text completion from the hugging face model pipeline.
+    """
+    # 2. Pass the prompt to the pipeline
+    responses = generator(prompt, max_length=150, num_return_sequences=1)
+    # The pipeline returns a list of dicts; we only need the generated_text
+    generated_text = responses[0]["generated_text"]
+    
+    # Optionally, if you want to remove the original prompt portion from the output, you can slice it out.
+    # For simplicity, we'll just return the entire generated text.
+    return generated_text
+
+# 3. Create and launch the Gradio interface
+demo = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
+    title="Hugging Face Chatbot",
+    description="Ask anything to this Hugging Face model!"
+)
+
+if __name__ == "__main__":
+    demo.launch()