import os
import yaml
import gradio as gr
from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.nebius import Nebius
from agno.tools.github import GithubTools
from agno.tools.exa import ExaTools
from agno.tools.thinking import ThinkingTools
from agno.tools.reasoning import ReasoningTools

# Load environment variables from .env if present
load_dotenv()

# Load prompts from YAML file
with open("hiring_prompts.yaml", "r", encoding="utf-8") as f:
    prompts = yaml.safe_load(f)

# Extract prompt sections
description_multi = prompts.get("description_for_multi_candidates", "")
instructions_multi = prompts.get("instructions_for_multi_candidates", "")
description_single = prompts.get("description_for_single_candidate", "")
instructions_single = prompts.get("instructions_for_single_candidate", "")

def analyze_multi(github_usernames, job_role, nebius_api_key, model_id, github_api_key, exa_api_key):
    """
    Analyze multiple GitHub users for a given job role.
    github_usernames: string with newline separated usernames.
    Returns a markdown report.
    """
    if not github_usernames or not job_role:
        return "Please provide GitHub usernames and job role."
    usernames = [u.strip() for u in github_usernames.split("\n") if u.strip()]
    if not usernames:
        return "No valid GitHub usernames found."
    query = f"Evaluate GitHub candidates for role '{job_role}': {', '.join(usernames)}"
    agent = Agent(
        description=description_multi,
        instructions=instructions_multi,
        model=Nebius(id=model_id, api_key=nebius_api_key),
        name="CandilyzerMulti",
        tools=[
            ThinkingTools(think=True, instructions="Strict GitHub candidate evaluation"),
            GithubTools(access_token=github_api_key),
            ExaTools(api_key=exa_api_key, include_domains=["github.com"], type="keyword"),
            ReasoningTools(add_instructions=True),
        ],
        markdown=True,
        show_tool_calls=True,
    )
    stream = agent.run(query, stream=True)
    output = ""
    for chunk in stream:
        if hasattr(chunk, "content") and isinstance(chunk.content, str):
            output += chunk.content
    return output

def analyze_single(github_username, linkedin_url, job_role, nebius_api_key, model_id, github_api_key, exa_api_key):
    """
    Analyze a single GitHub (and optional LinkedIn) candidate for a given job role.
    """
    if not github_username or not job_role:
        return "Please provide GitHub username and job role."
    query = f"Analyze candidate for {job_role}. GitHub: {github_username}"
    if linkedin_url:
        query += f", LinkedIn: {linkedin_url}"
    agent = Agent(
        description=description_single,
        instructions=instructions_single,
        model=Nebius(id=model_id, api_key=nebius_api_key),
        name="CandilyzerSingle",
        tools=[
            ThinkingTools(add_instructions=True),
            GithubTools(access_token=github_api_key),
            ExaTools(api_key=exa_api_key, include_domains=["linkedin.com", "github.com"], type="keyword"),
            ReasoningTools(add_instructions=True),
        ],
        markdown=True,
        show_tool_calls=True,
        add_datetime_to_instructions=True,
    )
    stream = agent.run(query, stream=True)
    output = ""
    for chunk in stream:
        if hasattr(chunk, "content") and isinstance(chunk.content, str):
            output += chunk.content
    return output

with gr.Blocks() as demo:
    gr.Markdown("# Candilyzer\nAI Candidate Analyzer using Agno and Nebius AI")
    with gr.Tab("Multi-Candidate Analyzer"):
        github_usernames = gr.Textbox(label="GitHub Usernames (one per line)", lines=4, placeholder="username1\nusername2\n...")
        job_role = gr.Textbox(label="Job Role", placeholder="Backend Engineer")
        nebius_api_key = gr.Textbox(label="Nebius API Key", type="password")
        model_id = gr.Textbox(label="Model ID", placeholder="meta-llama/Llama-3-70B-Instruct")
        github_api_key = gr.Textbox(label="GitHub API Key", type="password")
        exa_api_key = gr.Textbox(label="Exa API Key", type="password")
        multi_button = gr.Button("Analyze Candidates")
        multi_output = gr.Markdown()
        multi_button.click(analyze_multi, inputs=[github_usernames, job_role, nebius_api_key, model_id, github_api_key, exa_api_key], outputs=multi_output)

    with gr.Tab("Single Candidate Analyzer"):
        github_username = gr.Textbox(label="GitHub Username", placeholder="username")
        linkedin_url = gr.Textbox(label="LinkedIn Profile (optional)", placeholder="https://linkedin.com/in/...")
        job_role2 = gr.Textbox(label="Job Role", placeholder="ML Engineer")
        nebius_api_key2 = gr.Textbox(label="Nebius API Key", type="password")
        model_id2 = gr.Textbox(label="Model ID", placeholder="meta-llama/Llama-3-70B-Instruct")
        github_api_key2 = gr.Textbox(label="GitHub API Key", type="password")
        exa_api_key2 = gr.Textbox(label="Exa API Key", type="password")
        single_button = gr.Button("Analyze Candidate")
        single_output = gr.Markdown()
        single_button.click(analyze_single, inputs=[github_username, linkedin_url, job_role2, nebius_api_key2, model_id2, github_api_key2, exa_api_key2], outputs=single_output)

    demo.launch()