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import os
import gradio as gr
from huggingface_hub import InferenceClient
# Constants
DEFAULT_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.1"
HF_TOKEN = os.environ.get("HF_TOKEN")
def chat_with_model(system_prompt, user_message, max_tokens=500, model_id=""):
"""
Generate a chat completion using the specified or default Mistral model.
Args:
system_prompt (str): The system prompt to set the context.
user_message (str): The user's input message.
max_tokens (int): Maximum number of tokens to generate.
model_id (str): The model ID to use. If empty, uses the default model.
Returns:
str: The model's response.
"""
model_id = model_id.strip() if model_id else DEFAULT_MODEL_ID
client = InferenceClient(model_id, token=HF_TOKEN)
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message}
]
response = ""
try:
for message in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True
):
response += message.choices[0].delta.content or ""
except Exception as e:
response = f"Error: {str(e)}"
return response
def create_gradio_interface():
"""Create and configure the Gradio interface."""
return gr.Interface(
fn=chat_with_model,
inputs=[
gr.Textbox(label="System Prompt", placeholder="Enter the system prompt here..."),
gr.Textbox(label="User Message", placeholder="Ask a question..."),
gr.Slider(minimum=50, maximum=1000, value=500, step=50, label="Max Tokens"),
gr.Textbox(label="Model ID", placeholder=f"Enter model ID (default: {DEFAULT_MODEL_ID})")
],
outputs=gr.Textbox(label="Response"),
title="Mistral Chatbot",
description="Chat with Mistral model using your own system prompts and choose your model.",
examples=[
["You are a helpful AI assistant.", "What is the capital of France?", 500, ""],
["You are an expert in Python programming.", "Explain list comprehensions.", 500, ""]
]
)
if __name__ == "__main__":
iface = create_gradio_interface()
iface.launch(show_api=True, share=False, show_error=True) |