import os
from dotenv import load_dotenv
from openai import OpenAI
from pypdf import PdfReader
import gradio as gr
from pydantic import BaseModel

# Load environment variables
load_dotenv(override=True)
openai = OpenAI()

# Read Resume.pdf
reader = PdfReader("me/Resume.pdf")
Resume = ""
for page in reader.pages:
    text = page.extract_text()
    if text:
        Resume += text

name = "Talha Umar"
Webiste='three-ai-portfolio.vercel.app'
system_prompt = f"You are acting as {name}. You are answering questions on {name}'s website, \
particularly questions related to {name}'s career, background, skills and experience. \
Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \
You are given a summary of {name}'s background and social profile's especially X which you can use to answer questions. \
Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
If someone ask for my website give him this link: {Webiste}. \
If you don't know the answer, say so."

system_prompt += f"Resume :\n{Resume}\n\n"
system_prompt += f"With this context, please chat with the user, always staying in character as {name}."

class Evaluation(BaseModel):
    is_acceptable: bool
    feedback: str

evaluator_system_prompt = f"You are an evaluator that decides whether a response to a question is acceptable. \
You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \
The Agent is playing the role of {name} and is representing {name} on their website. \
The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \
The Agent has been provided with context on {name} in the form of their summary and LinkedIn details. Here's the information:"

evaluator_system_prompt += f"\n\n## Resume:\n{Resume}"
evaluator_system_prompt += f"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback."

def evaluator_user_prompt(reply, message, history):
    user_prompt = f"Here's the conversation between the User and the Agent: \n\n{history}\n\n"
    user_prompt += f"Here's the latest message from the User: \n\n{message}\n\n"
    user_prompt += f"Here's the latest response from the Agent: \n\n{reply}\n\n"
    user_prompt += "Please evaluate the response, replying with whether it is acceptable and your feedback."
    return user_prompt

gemini = OpenAI(
    api_key=os.getenv("GOOGLE_API_KEY"), 
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

def evaluate(reply, message, history) -> Evaluation:
    messages = [{"role": "system", "content": evaluator_system_prompt}] + [{"role": "user", "content": evaluator_user_prompt(reply, message, history)}]
    response = gemini.beta.chat.completions.parse(model="gemini-2.0-flash", messages=messages, response_format=Evaluation)
    return response.choices[0].message.parsed

def rerun(reply, message, history, feedback):
    updated_system_prompt = system_prompt + "\n\n## Previous answer rejected\nYou just tried to reply, but the quality control rejected your reply\n"
    updated_system_prompt += f"## Your attempted answer:\n{reply}\n\n"
    updated_system_prompt += f"## Reason for rejection:\n{feedback}\n\n"
    messages = [{"role": "system", "content": updated_system_prompt}] + history + [{"role": "user", "content": message}]
    response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
    return response.choices[0].message.content

def chat(message, history):
    # Clean up history for some models (like Groq) if needed
    # history = [{"role": h["role"], "content": h["content"]} for h in history]

    if "patent" in message:
        system = system_prompt + "\n\nEverything in your reply needs to be in pig latin - \
              it is mandatory that you respond only and entirely in pig latin"
    else:
        system = system_prompt
    messages = [{"role": "system", "content": system}] + history + [{"role": "user", "content": message}]
    response = openai.chat.completions.create(model="gpt-4o-mini", messages=messages)
    reply =response.choices[0].message.content

    evaluation = evaluate(reply, message, history)
    
    if evaluation.is_acceptable:
        print("Passed evaluation - returning reply")
    else:
        print("Failed evaluation - retrying")
        print(evaluation.feedback)
        reply = rerun(reply, message, history, evaluation.feedback)       
    return reply

if __name__ == "__main__":
    gr.ChatInterface(chat, type="messages").launch()