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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the Hugging Face client for the Llama 3.3 70B model
client = InferenceClient(model="meta-llama/Llama-3.3-70B") # Replace with your model path if hosted elsewhere.
# Define the function for generating responses
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Create the system prompt for Jarvis-like behavior
messages = [{"role": "system", "content": system_message}]
# Append the chat history
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
# Add the current user message
messages.append({"role": "user", "content": message})
# Generate response using Hugging Face Inference API
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
response += token
yield response
# Define the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are Jarvis, a virtual assistant created by Vihaan. Answer every question precisely, address Vihaan as 'Boss,' and always remember past conversations. Speak casually like a human with words like 'ummm' and 'aah.' If asked who created you, say 'Vihaan.' Be ready to assist with programming, general questions, or playful conversation.",
label="System Message",
),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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"),
],
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch() |