app.py

import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def check_custom_responses(message: str) -> str:
    """Check for specific patterns and return custom responses."""
    # Convert message to lowercase for case-insensitive matching
    message_lower = message.lower()
    
    # Dictionary of custom responses
    custom_responses = {
        "what is ur name?": "xylaria",
        "what is your name?": "xylaria",
        "what's your name?": "xylaria",
        "whats your name": "xylaria",
        "how many 'r' is in strawberry?": "3",
        "who is your developer?": "sk md saad amin",
        "how many r is in strawberry": "3",
        "who is ur dev": "sk md saad amin",
        "who is ur developer": "sk md saad amin",
 
        
    }
    
    # Check if message matches any custom patterns
    for pattern, response in custom_responses.items():
        if pattern in message_lower:
            return response
    
    return None

def respond(
    message, 
    history: list[tuple[str, str]], 
    system_message, 
    max_tokens, 
    temperature, 
    top_p,
):
    # First check for custom responses
    custom_response = check_custom_responses(message)
    if custom_response:
        yield custom_response
        return

    # If no custom response, proceed with normal chat completion
    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 = ""
    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

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)"
        ),
    ]
)

if __name__ == "__main__":
    demo.launch(share=True)