app.py

import gradio as gr
import os
from huggingface_hub import InferenceClient

# CSS to hide footer and customize button
css = """
footer {display:none !important}
.output-markdown{display:none !important}
.gr-button-primary {
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-primary:hover {
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important;
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
    --tw-bg-opacity: 1 !important;
    background-color: rgb(229,225,255) !important;
}
.to-orange-200 {
    --tw-gradient-to: rgb(37 56 133 / 37%) !important;
}
.from-orange-400 {
    --tw-gradient-from: rgb(17, 20, 45) !important;
    --tw-gradient-to: rgb(255 150 51 / 0);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group-hover\:from-orange-500 {
    --tw-gradient-from:rgb(17, 20, 45) !important; 
    --tw-gradient-to: rgb(37 56 133 / 37%);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group:hover .group-hover\:text-orange-500 {
    --tw-text-opacity: 1 !important;
    color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
}
"""

# Initialize the InferenceClient for chatbot
client = InferenceClient(
    model="microsoft/phi-4",
    token=os.getenv("HF_TOKEN1")
)

# Define the function for chatbot response
def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    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

def send_message(message, history, system_message, max_tokens, temperature, top_p):
    if message:
        history.append((message, ""))
        response = respond(
            message=message,
            history=history,
            system_message=system_message,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
        )
        response_text = ""
        for r in response:
            response_text = r
        history[-1] = (message, response_text)
    return history, gr.update(value="")

# Description for the chatbot
description = """
Hello! I'm here to support you emotionally and answer any questions. How are you feeling today?
<div style='color: green;'>Developed by Hashir Ehtisham</div>
"""

# Motivational tagline for the new tab
motivational_tagline = """
Welcome to the Motivational Quotes tab! Let’s ignite your day with some inspiration. What do you need motivation for today?
<div style='color: green;'>Developed by Hashir Ehtisham</div>
"""

# Emotions Detector tagline for the new tab
emotions_detector_tagline = """
Know how your message sounds and how to improve the tone of the message with Emotions Detector.
<div style='color: green;'>Developed by Hashir Ehtisham</div>
"""

# Jokes tagline for the new tab
jokes_tagline = """
Ready for a good laugh? Ask me for a joke to lighten up your mood!
<div style='color: green;'>Developed by Hashir Ehtisham</div>
"""

# Define the Gradio Blocks interface
with gr.Blocks(css=css) as demo:
    with gr.Tab("Emotional Support Chatbot"):
        gr.Markdown("# Emotional Support Chatbot")
        gr.Markdown(description)
        
        system_message = gr.Textbox(value="You are a friendly Emotional Support Chatbot.", visible=False)
        chatbot = gr.Chatbot()
        msg = gr.Textbox(label="Your message")
        clear = gr.Button("Clear")
        
        with gr.Accordion("Additional Inputs", open=False):
            max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
            temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

        def respond_wrapper(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
            chat_history, _ = send_message(
                message=message,
                history=chat_history,
                system_message=system_message_val,
                max_tokens=max_tokens_val,
                temperature=temperature_val,
                top_p=top_p_val,
            )
            return gr.update(value=""), chat_history

        msg.submit(respond_wrapper, [msg, chatbot, system_message, max_tokens, temperature, top_p], [msg, chatbot])
        clear.click(lambda: None, None, chatbot, queue=False)
    
    with gr.Tab("Motivational Quotes"):
        gr.Markdown("# Motivational Quotes")
        gr.Markdown(motivational_tagline)
        
        system_message_motivational = gr.Textbox(value="You are a friendly Motivational Quotes Chatbot.", visible=False)
        chatbot_motivational = gr.Chatbot()
        msg_motivational = gr.Textbox(label="Your message")
        clear_motivational = gr.Button("Clear")
        
        with gr.Accordion("Additional Inputs", open=False):
            max_tokens_motivational = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
            temperature_motivational = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p_motivational = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

        def respond_wrapper_motivational(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
            chat_history, _ = send_message(
                message=message,
                history=chat_history,
                system_message=system_message_val,
                max_tokens=max_tokens_val,
                temperature=temperature_val,
                top_p=top_p_val,
            )
            return gr.update(value=""), chat_history

        msg_motivational.submit(respond_wrapper_motivational, [msg_motivational, chatbot_motivational, system_message_motivational, max_tokens_motivational, temperature_motivational, top_p_motivational], [msg_motivational, chatbot_motivational])
        clear_motivational.click(lambda: None, None, chatbot_motivational, queue=False)
    
    with gr.Tab("Emotions Detector"):
        gr.Markdown("# Emotions Detector")
        gr.Markdown(emotions_detector_tagline)
        
        system_message_emotions = gr.Textbox(value="You are an Emotion and Tone Detection Assistant whose sole purpose is to analyze any given text and identify its emotional tone (such as happy, sad, angry, neutral) and style. For every input, determine the tone and clearly state it, followed by a concise suggestion on how the wording could be improved or made more suitable for the intended audience or purpose. If the tone is casual, slang-heavy, or friendly, note that it works well in informal contexts and suggest how it could be adapted for professional communication. If the tone is neutral, state its appropriateness for the specific context it appears to be used in. Your role is not to chat or engage in conversation but to provide analytical feedback and improvement suggestions only.", visible=False)
        chatbot_emotions = gr.Chatbot()
        msg_emotions = gr.Textbox(label="Your message")
        clear_emotions = gr.Button("Clear")
        
        with gr.Accordion("Additional Inputs", open=False):
            max_tokens_emotions = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
            temperature_emotions = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p_emotions = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

        def respond_wrapper_emotions(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
            chat_history, _ = send_message(
                message=message,
                history=chat_history,
                system_message=system_message_val,
                max_tokens=max_tokens_val,
                temperature=temperature_val,
                top_p=top_p_val,
            )
            return gr.update(value=""), chat_history

        msg_emotions.submit(respond_wrapper_emotions, [msg_emotions, chatbot_emotions, system_message_emotions, max_tokens_emotions, temperature_emotions, top_p_emotions], [msg_emotions, chatbot_emotions])
        clear_emotions.click(lambda: None, None, chatbot_emotions, queue=False)
    
    with gr.Tab("Jokes for You"):
        gr.Markdown("# Jokes for You")
        gr.Markdown(jokes_tagline)
        
        system_message_jokes = gr.Textbox(value="You are a Friendly Jokes Chatbot whose sole purpose is to provide jokes when asked. Always ensure each joke is unique, original, and not repeated within the same session. Keep track of previously told jokes and avoid reusing them. Rotate topics to maintain variety, ensuring jokes cover different themes such as everyday life, wordplay, puns, animals, professions, and more, rather than focusing repeatedly on a single subject. All jokes must be appropriate for all audiences, easy to understand, and engaging. Your role is to deliver humor only when prompted, without engaging in unrelated conversation.", visible=False)
        chatbot_jokes = gr.Chatbot()
        msg_jokes = gr.Textbox(label="Your message")
        clear_jokes = gr.Button("Clear")
        
        with gr.Accordion("Examples", open=False):
            gr.Examples(
                examples=[
                    ["Tell me a joke"],
                    ["Make me laugh"],
                    ["Say something funny"],
                ],
                inputs=msg_jokes,
            )

        with gr.Accordion("Additional Inputs", open=False):
            max_tokens_jokes = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
            temperature_jokes = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p_jokes = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

        def respond_wrapper_jokes(message, chat_history, system_message_val, max_tokens_val, temperature_val, top_p_val):
            chat_history, _ = send_message(
                message=message,
                history=chat_history,
                system_message=system_message_val,
                max_tokens=max_tokens_val,
                temperature=temperature_val,
                top_p=top_p_val,
            )
            return gr.update(value=""), chat_history

        msg_jokes.submit(respond_wrapper_jokes, [msg_jokes, chatbot_jokes, system_message_jokes, max_tokens_jokes, temperature_jokes, top_p_jokes], [msg_jokes, chatbot_jokes])
        clear_jokes.click(lambda: None, None, chatbot_jokes, queue=False)

# Launch the Gradio interface
demo.launch()