adding

app.py

@@ -1,63 +1,155 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import torch
+from transformers import pipeline
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# Inference client setup
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+pipe = pipeline("text-generation", "microsoft/Phi-3-mini-4k-instruct", torch_dtype=torch.bfloat16, device_map="auto")
 
+# Global flag to handle cancellation
+stop_inference = False
 
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    system_message="You are a friendly Chatbot.",
+    max_tokens=512,
+    temperature=0.7,
+    top_p=0.95,
+    use_local_model=False,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    global stop_inference
+    stop_inference = False  # Reset cancellation flag
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Initialize history if it's None
+    if history is None:
+        history = []
 
-    messages.append({"role": "user", "content": message})
+    if use_local_model:
+        # local inference 
+        messages = [{"role": "system", "content": system_message}]
+        for val in history:
+            if val[0]:
+                messages.append({"role": "user", "content": val[0]})
+            if val[1]:
+                messages.append({"role": "assistant", "content": val[1]})
+        messages.append({"role": "user", "content": message})
 
-    response = ""
+        response = ""
+        for output in pipe(
+            messages,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=True,
+            top_p=top_p,
+        ):
+            if stop_inference:
+                response = "Inference cancelled."
+                yield history + [(message, response)]
+                return
+            token = output['generated_text'][-1]['content']
+            response += token
+            yield history + [(message, response)]  # Yield history + new response
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    else:
+        # API-based inference 
+        messages = [{"role": "system", "content": system_message}]
+        for val in history:
+            if val[0]:
+                messages.append({"role": "user", "content": val[0]})
+            if val[1]:
+                messages.append({"role": "assistant", "content": val[1]})
+        messages.append({"role": "user", "content": message})
 
-        response += token
-        yield response
+        response = ""
+        for message_chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            if stop_inference:
+                response = "Inference cancelled."
+                yield history + [(message, response)]
+                return
+            if stop_inference:
+                response = "Inference cancelled."
+                break
+            token = message_chunk.choices[0].delta.content
+            response += token
+            yield history + [(message, response)]  # Yield history + new response
 
+
+def cancel_inference():
+    global stop_inference
+    stop_inference = True
+
+# Custom CSS for a fancy look
+custom_css = """
+#main-container {
+    background-color: #f0f0f0;
+    font-family: 'Arial', sans-serif;
+}
+.gradio-container {
+    max-width: 700px;
+    margin: 0 auto;
+    padding: 20px;
+    background: white;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    border-radius: 10px;
+}
+.gr-button {
+    background-color: #4CAF50;
+    color: white;
+    border: none;
+    border-radius: 5px;
+    padding: 10px 20px;
+    cursor: pointer;
+    transition: background-color 0.3s ease;
+}
+.gr-button:hover {
+    background-color: #45a049;
+}
+.gr-slider input {
+    color: #4CAF50;
+}
+.gr-chat {
+    font-size: 16px;
+}
+#title {
+    text-align: center;
+    font-size: 2em;
+    margin-bottom: 20px;
+    color: #333;
+}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
+# Define the interface
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("<h1 style='text-align: center;'>🌟 Fancy AI Chatbot 🌟</h1>")
+    gr.Markdown("Interact with the AI chatbot using customizable settings below.")
+
+    with gr.Row():
+        system_message = gr.Textbox(value="You are a friendly Chatbot.", label="System message", interactive=True)
+        use_local_model = gr.Checkbox(label="Use Local Model", value=False)
+
+    with gr.Row():
+        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+    chat_history = gr.Chatbot(label="Chat")
+
+    user_input = gr.Textbox(show_label=False, placeholder="Type your message here...")
+
+    cancel_button = gr.Button("Cancel Inference", variant="danger")
+
+    # Adjusted to ensure history is maintained and passed correctly
+    user_input.submit(respond, [user_input, chat_history, system_message, max_tokens, temperature, top_p, use_local_model], chat_history)
+
+    cancel_button.click(cancel_inference)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=False)  # Remove share=True because it's not supported on HF Spaces