import gradio as gr
import os
from huggingface_hub import InferenceClient
import pandas as pd

# Direct link to the image
image_url = "https://drive.google.com/uc?export=view&id=1AB7sFKxPLkJE_RmyUDap6fFaDlu1XGJl"

# Define the system message
system_message = """
You are a Career Counseling Chatbot. Analyze the student's academic performance and extracurricular activities to provide career guidance. Based on the provided data, respond in the following format and must include the following headings:
# **Student's Primary Interest with Reason**
# **Career Opportunities in the field**
# **Universities in Pakistan for related field**
# **Conclusion with name of field**
Ensure that the analysis is based on the student's performance in subjects and extracurriculars, and suggest relevant career options with details on possible high ranking universities in Pakistan.
"""

# CSS to hide footer, customize button, and center image
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;
}
#image-container {
    display: flex;
    justify-content: center;
    align-items: center;
    height: auto; /* Adjust the height as needed */
    margin-top: 20px; /* Adjust the margin as needed */
}
#compass-image {
    max-width: 800px; /* Adjust the width as needed */
    max-height: 600px; /* Adjust the height as needed */
    object-fit: contain; /* Maintains aspect ratio */
}
"""

# Initialize the InferenceClient for chatbot
client = InferenceClient(
    model="HuggingFaceH4/zephyr-7b-beta",
    token=os.getenv("HF_TOKEN")
)

# Global variable to store chat history for the current session
current_chat_history = []

# Define the function for chatbot response
def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    global current_chat_history
    
    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]})
            current_chat_history.append(f"Assistant: {val[1]}")
    
    messages.append({"role": "user", "content": message})
    current_chat_history.append(f"User: {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

    # Append the assistant's final response to the history
    current_chat_history.append(f"Assistant: {response}")

def download_chat_history():
    # Join the current chat history into a single string
    history_str = "\n".join(current_chat_history)
    # Save the chat history to a text file
    with open("chat_history.txt", "w") as f:
        f.write(history_str)
    return "chat_history.txt"

def clear_chat_history():
    # Reset the current chat history
    global current_chat_history
    current_chat_history.clear()  # Clear the chat history
    return "Chat history cleared."
        
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
        # Apply HTML formatting to headings
        formatted_response_text = response_text.replace(
            "Student's Primary Interest with Reason:", "<h2><strong>Student's Primary Interest with Reason</strong></h2>"
        ).replace(
            "Career Opportunities in the field:", "<h2><strong>Career Opportunities in the field</strong></h2>"
        ).replace(
            "Universities in Pakistan for related field:", "<h2><strong>Universities in Pakistan for related field</strong></h2>"
        ).replace(
            "Conclusion with name of field:", "<h2><strong>Conclusion with name of field</strong></h2>"
        )
        history[-1] = (message, formatted_response_text)
    return history, gr.update(value="")

# Excel reading function
def read_excel(file):
    df = pd.read_excel(file.name)
    return df.to_string()

# Create the Gradio interface
with gr.Blocks(css=css) as demo:
    # Introduction Tab
    with gr.Tab("Career Compass"):
        with gr.Row(elem_id="image-container"):
            gr.Image(image_url, elem_id="compass-image")
        
        gr.Markdown("# **Career Compass**")
        gr.Markdown("### **Developed by Hashir Ehtisham**")
        gr.Markdown("""
        **Career Compass** is a cutting-edge AI-powered tool designed to provide personalized career guidance based on students' academic performance and extracurricular activities. The key features of this tool include:
        - **Personalized Analysis:** Delivers career advice tailored to individual student profiles.
        - **Streamlined Interface:** Simple and intuitive user experience.
        - **Detailed Reports:** Offers insights into suitable career paths, relevant universities, and job opportunities.
        - **General Guidance & Emotional Support:** Talk to AI for General Career Guidance and also lighten your mood. 
        **Libraries Used:**
        - **Gradio:** For creating the user interface.
        - **Pandas:** For reading and analyzing Excel files.
        - **Hugging API and LLM:** Zephyr-7b-beta For utilizing state-of-the-art language models.
        
        **How It Works:**
        - **Detailed Analysis**
        1. Upload your academic records.
        2. Input your query regarding career guidance.
        3. Get detailed recommendations and potential career paths!
         - **General Guidance & Emotional Support**
        1. Enter your query and doubts about choosing University majors and Chatbot will guide you about the right choice.
        2. Ask about Career Opportunities and scope of different fields to get unbaised AI analyzed answer and recommendations and potential career paths!
        3. IF you ever feel sad, anxious or depressed, talk to Career Compass and it will console you like a friend.
        """)
    
    # Detailed Analysis Tab
    with gr.Tab("Detailed Analysis"):
        gr.Markdown("# Detailed Analysis")
        gr.Markdown("Get personalized career guidance based on academic performance and extracurricular activities with Detailed Analysis.\n<div style='color: green;'>Developed by Hashir Ehtisham</div>")
        
        system_message_career = gr.Textbox(value=system_message, visible=False)
        chatbot_career = gr.Chatbot()
        msg_career = gr.Textbox(label="Enter the Excel Copied Data here")
        
        with gr.Row():
            clear_career = gr.Button("New Chat")
            download_button = gr.Button("Download Chat History")
            submit_career = gr.Button("Submit")

        with gr.Row():
            download_output = gr.File(label="Download")
            download_button.click(download_chat_history, outputs=download_output)

        with gr.Row():
            clear_button = gr.Button("Clear Chat History")
            status_output = gr.Textbox(label="Status", interactive=False)  # Add a Textbox for status output
            clear_button.click(clear_chat_history, outputs=status_output)  # Use the status Textbox for output
        
        with gr.Accordion("Additional Inputs", open=False):
            max_tokens_career = gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens")
            temperature_career = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p_career = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

        def respond_wrapper_career(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), gr.update(value="")

        submit_career.click(
            respond_wrapper_career,
            inputs=[msg_career, chatbot_career, system_message_career, max_tokens_career, temperature_career, top_p_career],
            outputs=[chatbot_career, msg_career],
        )
        
        clear_career.click(lambda: None, None, chatbot_career, queue=False)

    # File Upload Tab
    with gr.Tab("Upload Data"):
        gr.Markdown("# Upload Data")
        file_input = gr.File(label="Upload Excel file")
        excel_output = gr.Textbox(label="Excel Content")
        file_input.change(read_excel, inputs=file_input, outputs=excel_output)

    # Simple Chatbot Tab (new tab integration)
    with gr.Tab("General Guidance & Emotional Support"):
        gr.Markdown("# General Guidance & Emotional Support")
        gr.Markdown("""
        A compassionate career counseling chatbot providing personalized guidance on career paths and emotional support for your journey.
        <div style='color: green;'>Developed by Hashir Ehtisham</div>
        """)

        system_message_simple = gr.Textbox(value="You are an AI powered chatbot named as Career Compass built by Hashir Ehtisham who is a student of APS DHA II Sec -D to help students, teachers, and parents find the best career paths based on students' interests and academic performance.", visible=False)
        chatbot_simple = gr.Chatbot()
        msg_simple = gr.Textbox(label="Type a message")
        
        with gr.Row():
            clear_simple = gr.Button("Clear")
            submit_simple = gr.Button("Submit")
        
        with gr.Accordion("Additional Inputs", open=False):
            max_tokens_simple = gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens")
            temperature_simple = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
            top_p_simple = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
        
        def respond_wrapper_simple(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 chat_history

        submit_simple.click(
            respond_wrapper_simple,
            inputs=[
                msg_simple,
                chatbot_simple,
                system_message_simple,
                max_tokens_simple,
                temperature_simple,
                top_p_simple,
            ],
            outputs=[chatbot_simple],
        )

        clear_simple.click(lambda: None, None, chatbot_simple)

# Launch the Gradio app
demo.launch()