app_hf.py

import os
import gradio as gr
from huggingface_hub import InferenceClient

# Initialize Hugging Face Inference Client
HF_API_KEY = os.environ.get("HF_API_KEY")
HF_MODEL_NAME = os.environ.get("HF_MODEL_NAME")

client = InferenceClient(model=HF_MODEL_NAME, token=HF_API_KEY)

# Streaming career coach logic
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    field_of_interest,
    dream_job,
    current_qualifications,
    likes,
    skills,
    ask_for_path_suggestions,
    max_tokens,
    temperature,
    top_p,
):
    # Construct system message using inputs
    enhanced_system_message = (
        f"{system_message}\n\n"
        f"Field of Interest: {field_of_interest}\n"
        f"Dream Job: {dream_job}\n"
        f"Current Qualifications: {current_qualifications}\n"
        f"Likes and Interests: {likes}\n"
        f"Skills: {skills}\n"
    )

    if ask_for_path_suggestions:
        enhanced_system_message += " The user would also like suggestions for potential career paths based on their background and interests."

    messages = [{"role": "system", "content": enhanced_system_message}]

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

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

    # Generate the streamed response
    response = ""
    for msg in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True,
    ):
        token = msg.choices[0].delta.content
        if token:
            response += token
            yield response

# Gradio Interface for Career Coach
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(
            label="Instructions to Bot",
            value="You are a friendly and insightful AI career coach. You help users discover potential career paths, suggest steps to reach their dream jobs, and offer advice based on their interests, qualifications, and skills.",
            lines=3,
        ),
        gr.Textbox(label="Field of Interest", placeholder="e.g., technology, healthcare, trades, education..."),
        gr.Textbox(label="Dream Job", placeholder="e.g., software developer, nurse, plumber, teacher..."),
        gr.Textbox(label="Current Qualifications", placeholder="e.g., high school diploma, college degree, self-taught..."),
        gr.Textbox(label="Likes & Interests", placeholder="e.g., helping others, solving puzzles, building things..."),
        gr.Textbox(label="Skills", placeholder="e.g., writing, coding, teamwork, analysis..."),
        gr.Checkbox(label="Ask for Career Path Suggestions", value=True),
        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)"),
    ],
    title="Career Coach – Explore Your Path!",
    description=(
        "This app helps you explore your career path and plan how to reach your dream job. "
        "Provide details about your background, interests, and goals, then enter a message to ask the assistant for help. "
        "Powered by Hugging Face Inference and GPT. Developed by wn. Disclaimer: AI may make mistakes. Use with caution."
    ),
)

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