Update qachatbot.py

qachatbot.py

@@ -1,30 +1,104 @@
 import gradio as gr
-from transformers import AutoTokenizer, TextIteratorStreamer
+from transformers import pipeline, AutoTokenizer
 
-# Define the models and their configurations
-model_name = "phi-2"  # Replace with the actual model name
-model_configuration = {
-    "toeknizer_kwargs": {'model_id': 'susnato/phi-2', 'prompt_template': 'Instruct:{instruction}\nOutput:'}  # Replace with the actual tokenizer configuration
-}
+# Define examples and model configuration
+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",
+]
 
-# Load the tokenizer
+# Define the model and its tokenizer
+model_name = "susnato/phi-2"  # Replace with your actual model identifier
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})
+generator = pipeline("text-generation", model=model_name, tokenizer=tokenizer)
+
+def run_generation(user_text, top_p, temperature, top_k, max_new_tokens, performance):
+    prompt = f"Instruct:{user_text}\nOutput:"
+    response = generator(prompt, max_length=max_new_tokens, top_p=top_p, temperature=temperature, top_k=top_k)[0]["generated_text"]
+    return response, "N/A"  # Replace "N/A" with actual performance metrics if available
+
+def reset_textbox(*args):
+    return "", "", ""
 
-# Define the Gradio interface
 def main():
-    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.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):
-                button_clear = gr.Button(value="Clear")
-                button_submit = gr.Button(value="Submit")
+                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],
+        )
+
+    return demo
 
-# Run the Gradio interface
-iface = gr.Interface(fn=main, inputs=user_text, outputs=model_output, performance=performance, live=True)
-iface.launch()
+if __name__ == "__main__":
+    iface = main()
+    iface.launch(share=True)