initial commit

README.md

@@ -1,5 +1,5 @@
 ---
-title: Gradio Chatbot
+title: Buttler7
 emoji: 💬
 colorFrom: yellow
 colorTo: purple
@@ -7,6 +7,7 @@ sdk: gradio
 sdk_version: 5.0.1
 app_file: app.py
 pinned: false
+license: mit
 ---
 
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,64 +1,67 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
+# This Gradio app simulates a smart assistant similar to Alexa, but with the capabilities of LLaMA 3.2.
+# The assistant listens for the wake word "Hey Butler" and responds to user commands.
+# It can also sync with Bluetooth devices and perform tasks based on user input.
+# The assistant can be trained after each conversation to improve its responses.
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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]})
+import gradio as gr
+import numpy as np
+import transformers_js_py  # Assuming this is a fictional library for LLaMA 3.2
 
-    messages.append({"role": "user", "content": message})
+# Initialize the LLaMA 3.2 model
+model = transformers_js_py.LLaMA32Model()
 
-    response = ""
+# Function to simulate the assistant's response
+def assistant_response(user_input, history):
+    # Check if the wake word is present
+    if "Hey Butler" in user_input:
+        # Generate a response using the LLaMA 3.2 model
+        response = model.generate_response(user_input, history)
+        # Simulate Bluetooth sync
+        sync_bluetooth()
+        return response
+    else:
+        return "Please say 'Hey Butler' to activate the assistant."
 
-    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
+# Function to simulate Bluetooth sync
+def sync_bluetooth():
+    # Simulate Bluetooth sync (this is a placeholder function)
+    print("Bluetooth synced successfully.")
 
-        response += token
-        yield response
+# Function to train the model after each conversation
+def train_model(user_input, assistant_response, history):
+    # Train the model with the new conversation data
+    model.train(user_input, assistant_response, history)
 
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    # Create a chatbot component
+    chatbot = gr.Chatbot(type="messages")
+    # Create a textbox for user input
+    user_input = gr.Textbox(label="User Input")
+    # Create a button to clear the chat history
+    clear_button = gr.Button("Clear History")
 
-"""
-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 event listener for user input
+    user_input.submit(
+        assistant_response,
+        inputs=[user_input, chatbot],
+        outputs=[chatbot],
+        queue=False
+    ).then(
+        train_model,
+        inputs=[user_input, chatbot, chatbot],
+        outputs=None,
+        queue=False
+    )
 
+    # Define the event listener for the clear button
+    clear_button.click(
+        lambda: None,
+        None,
+        chatbot,
+        queue=False
+    )
 
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_error=True)

requirements.txt

@@ -1 +1 @@
-huggingface_hub==0.25.2
+numpy