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gradio/app.py

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+import gradio as gr
+
+examples = [
+    "Give me a recipe for pizza with pineapple",
+    "Write me a tweet about the new OpenVINO release",
+    "Explain the difference between CPU and GPU",
+    "Give five ideas for a great weekend with family",
+    "Do Androids dream of Electric sheep?",
+    "Who is Dolly?",
+    "Please give me advice on how to write resume?",
+    "Name 3 advantages to being a cat",
+    "Write instructions on how to become a good AI engineer",
+    "Write a love letter to my best friend",
+]
+
+def run_generation(user_text, top_p, temperature, top_k, max_new_tokens, performance):
+    # Example response, replace with actual model call
+    response = f"Generated text for: {user_text}"
+    performance = "N/A"  # Replace with actual performance metrics if available
+    return response, performance
+
+def reset_textbox(*args):
+    return "", "", ""
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        "# Question Answering with OpenVINO\n"
+        "Provide instruction which describes a task below or select among predefined examples and model writes response that performs requested task."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            user_text = gr.Textbox(
+                placeholder="Write an email about an alpaca that likes flan",
+                label="User instruction",
+            )
+            model_output = gr.Textbox(label="Model response", interactive=False)
+            performance = gr.Textbox(label="Performance", lines=1, interactive=False)
+            with gr.Column(scale=1):
+                button_clear = gr.Button(value="Clear")
+                button_submit = gr.Button(value="Submit")
+            gr.Examples(examples, user_text)
+        with gr.Column(scale=1):
+            max_new_tokens = gr.Slider(
+                minimum=1,
+                maximum=1000,
+                value=256,
+                step=1,
+                interactive=True,
+                label="Max New Tokens",
+            )
+            top_p = gr.Slider(
+                minimum=0.05,
+                maximum=1.0,
+                value=0.92,
+                step=0.05,
+                interactive=True,
+                label="Top-p (nucleus sampling)",
+            )
+            top_k = gr.Slider(
+                minimum=0,
+                maximum=50,
+                value=0,
+                step=1,
+                interactive=True,
+                label="Top-k",
+            )
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=5.0,
+                value=0.8,
+                step=0.1,
+                interactive=True,
+                label="Temperature",
+            )
+
+    user_text.submit(
+        run_generation,
+        [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+        [model_output, performance],
+    )
+    button_submit.click(
+        run_generation,
+        [user_text, top_p, temperature, top_k, max_new_tokens, performance],
+        [model_output, performance],
+    )
+    button_clear.click(
+        reset_textbox,
+        [user_text, model_output, performance],
+        [user_text, model_output, performance],
+    )
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()

gradio/requirements.txt

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+gradio