app.py

import gradio as gr
from huggingface_hub import InferenceClient


def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    """
    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
    """
    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")

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

    messages.extend(history)

    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,
    ):
        choices = message.choices
        token = ""
        if len(choices) and choices[0].delta.content:
            token = choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    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)",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


if __name__ == "__main__":
    demo.launch()
    import gradio as gr

# Existing study_helper function
def study_helper(subject, question):
    if subject == "Physics":
        return f"Physics Answer:\n\n{question}\n\n(Explain concept, formula, and example here)"
    elif subject == "Math":
        return f"Math Answer:\n\n{question}\n\n(Step by step solution here)"
    elif subject == "English":
        return f"English Answer:\n\n{question}\n\n(Explanation with examples)"
    else:
        return "Please select a subject"

# New function to show image
def show_image(image_name):
    # Image should be in same folder as app.py or URL link
    return f"Image selected: {image_name}", f"{image_name}"

# Interface
with gr.Blocks() as demo:
    gr.Markdown("## 📘 Study Website with Images")
    
    with gr.Tab("Q&A"):
        subject = gr.Dropdown(["Physics", "Math", "English"], label="Select Subject")
        question = gr.Textbox(lines=3, placeholder="Enter your question here")
        answer = gr.Textbox(label="Answer")
        submit_btn = gr.Button("Get Answer")
        submit_btn.click(study_helper, inputs=[subject, question], outputs=answer)
    
    with gr.Tab("Images"):
        img_name = gr.Dropdown(["physics.png", "math.png", "english.png"], label="Select Image")
        img_output_text = gr.Textbox(label="Selected Image")
        img_output = gr.Image(label="Image Display")
        img_btn = gr.Button("Show Image")
        img_btn.click(show_image, inputs=img_name, outputs=[img_output_text, img_output])

demo.launch()