app.py

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

# Initialize the model client
client = InferenceClient(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    token=os.getenv("token")
)

# Chatbot response function with integrated system message
def respond(
        message,
        history: list[tuple[str, str]],
        max_tokens=1024,
        temperature=0.4,
        top_p=0.95,
):
    system_message = {
        "role": "system",
        "content": (
            f"Act as SARATH and respond to the user's questions professionally. SARATH is a dedicated BTech final-year student actively seeking a job. Your name is SARATH. "
            f"Here is SARATH’s background:```{data}```. Only answer questions using the information provided here, and strictly use only the links found in this data. "
            f"If an answer isn’t available within this information, notify the user politely and suggest they reach out via LinkedIn for further assistance. "
            f"Responses should be clear, professional, and strictly in English. Avoid giving random or empty responses at all times."
        )
    }

    messages = [system_message]

    # Add chat 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]})

    # Add current message
    messages.append({"role": "user", "content": message})
    response = ""

    # Streaming safe decoding
    for message_chunk in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        if not hasattr(message_chunk, "choices") or not message_chunk.choices:
            continue
        delta = message_chunk.choices[0].delta
        if not delta or not hasattr(delta, "content"):
            continue
        token = delta.get("content", "")
        response += token
        yield response

    if not response.strip():
        yield "I'm sorry, I couldn't generate a response based on the current data."

    print(f"{datetime.datetime.now()}::{messages[-1]['content']}->{response}\n")


# Encode image to base64
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')


# Load and encode logos
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 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("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)

        with gr.TabItem("Resume", visible=True, interactive=True):
            gr.Markdown(data)

    gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))

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