from huggingface_hub import InferenceClient
from resume import data
import markdowm as md
import gradio as gr
import base64
import datetime

client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")

# Chatbot response function with integrated system message
def respond(
        message,
        history: list[tuple[str, str]],
        max_tokens=1024,
        temperature=0.7,
        top_p=0.95,
):
    # System message defining assistant behavior
    system_message = {
        "role": "system",
        "content": f"Act and chat as SARATH to thw user question. SARATH is a professional fresher seeking a job and pursuing Btech-4th year. your name is SARATH."
                   f"Here is about SARATH:```{data}```. You should answer questions based on this information only and other context convey  a message that it havent found any info on my knowledge base contact me on linked."
                   f"strictly prohibit the random respons or empty respons and speak in English"
    }

    messages = [system_message]

    # Adding conversation history
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
            
    # print(f"{datetime.datetime.now()}::{{'role': 'user', 'content': val[0]}}->{{'role': 'user', 'content': val[1]}}")
    
    # Adding the current user input
    messages.append({"role": "user", "content": message})
    
    response = ""

    # Streaming the response from the 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
        
    print(f"{datetime.datetime.now()}::{messages[-1]["content"]}->{response}\n")
    
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Encode the images
github_logo_encoded = encode_image("Images/github-logo.png")
linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
website_logo_encoded = encode_image("Images/ai-logo.png")

# Gradio interface with additional sliders for control
with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as main:
    gr.Markdown(md.title)
    with gr.Tabs():
        with gr.TabItem("Resume", visible=True, interactive=True):
            gr.Markdown(data)

        with gr.TabItem("My2.0", visible=True, interactive=True):
            gr.ChatInterface(respond,
                             chatbot=gr.Chatbot(height=500),
                             examples=["Tell me about yourself",
                                       'Can you walk me through some of your recent projects and explain the role you played in each?',
                                       "What specific skills do you bring to the table that would benefit our company's AI/ML initiatives?",
                                       "How do you stay updated with the latest trends and advancements in AI and Machine Learning?",
                                      ]
                            )
            gr.Markdown(md.description)
    
    gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))

if __name__ == "__main__":
    main.launch(share=True)