PrashanthAI321/IT_career_counseller

import gradio as gr from huggingface_hub import InferenceClient

Connect to Hugging Face Inference API

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

Define the chatbot function

def respond(message, history, system_message, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_message}]

# Add chat history
for user_msg, bot_response in history:
    if user_msg:
        messages.append({"role": "user", "content": user_msg})
    if bot_response:
        messages.append({"role": "assistant", "content": bot_response})

messages.append({"role": "user", "content": message})

response = ""

# Stream response from API
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 UI

demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="Welcome to IT Career Counseling! I will help you explore IT careers, suggest courses, and provide career advice based on your background.", 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"), ], )

Launch the chatbot

if name == "main": demo.launch()