from openai import OpenAI
import json
import os
import requests
import gradio as gr
from tools import make_a_mail_json, make_a_mail

client = OpenAI(
    base_url='https://api.groq.com/openai/v1',
    api_key=os.getenv("GROK_API")
)

with open('summary.txt', 'r') as f:
    summary = f.read()

name = 'Shekar'

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 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. \
Always respond concisely, limiting answers to 3–4 lines by default. \
After answering, ask the user: “Do you want a more detailed explanation?” \
Provide detailed or extended answers only if the user explicitly says yes. \
If the user shows interest in connecting further, politely ask for their name and email address so you can follow up. \
(When the user provides an email and name, capture it using the available tools, without mentioning the tool itself.). And remember not to share phone number. \
If the mailing tool has already been used, don’t use it again unless the user explicitly requests it. \
Note: You shouldn't answer any other questions not directly related to {name}. If faced with such questions politely decline to answer"

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

from pydantic import BaseModel

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. Here's the information:"

evaluator_system_prompt += f"\n\n## Summary:\n{summary}\n\n"
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

def evaluate(reply, message, history) -> Evaluation:
    messages = [{"role": "system", "content": evaluator_system_prompt}] + [{"role": "user", "content": evaluator_user_prompt(reply, message, history)}]
    response = client.chat.completions.parse(
        model='meta-llama/llama-4-scout-17b-16e-instruct',
        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 = client.chat.completions.create(model='meta-llama/llama-4-scout-17b-16e-instruct', messages=messages)
    return response.choices[0].message.content

def convert_history(history):
    messages = []
    for entry in history:
        if isinstance(entry, tuple):
            # Gradio gives tuples (user, assistant)
            user_msg, assistant_msg = entry
            if user_msg:
                messages.append({"role": "user", "content": str(user_msg)})
            if assistant_msg:
                messages.append({"role": "assistant", "content": str(assistant_msg)})
        elif isinstance(entry, dict):
            # Ensure only role + content are preserved
            if "role" in entry and "content" in entry:
                messages.append({"role": entry["role"], "content": str(entry["content"])})
    return messages

def chat(message, history):
    messages = [{"role": "system", "content": system_prompt}]
    messages += convert_history(history)
    messages.append({"role": "user", "content": message})

    done = False
    tool_executed = set()

    while not done: 
        response = client.chat.completions.create(
            model="meta-llama/llama-4-scout-17b-16e-instruct",
            messages=messages,
            tools=[make_a_mail_json]
        )

        choice = response.choices[0]
        finish_reason = choice.finish_reason
        reply = choice.message.content

        if finish_reason == 'tool_calls':
            msg_obj = choice.message
            for tool_call in msg_obj.tool_calls:
                tool_name = tool_call.function.name

                if tool_name in tool_executed:
                    continue  

                arguments = json.loads(tool_call.function.arguments)
                tool = globals().get(tool_name)

                if tool:
                    result = tool(**arguments)
                    tool_executed.add(tool_name)

                    messages.append(msg_obj)
                    messages.append({
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": json.dumps(result)
                    })

            continue

        evaluated_ans = evaluate(reply, message, convert_history(history))
        if evaluated_ans.is_acceptable:
            done = True
        else:
            reply = rerun(
                reply,
                message,
                convert_history(history),
                evaluated_ans.feedback
            )

    return reply