# from dotenv import load_dotenv
# from openai import OpenAI
# import json
# import os
# import requests
# from pypdf import PdfReader
# import gradio as gr


# load_dotenv(override=True)

# def push(text):
#     requests.post(
#         "https://api.pushover.net/1/messages.json",
#         data={
#             "token": os.getenv("PUSHOVER_TOKEN"),
#             "user": os.getenv("PUSHOVER_USER"),
#             "message": text,
#         }
#     )


# def record_user_details(email, name="Name not provided", notes="not provided"):
#     push(f"Recording {name} with email {email} and notes {notes}")
#     return {"recorded": "ok"}

# def record_unknown_question(question):
#     push(f"Recording {question}")
#     return {"recorded": "ok"}

# record_user_details_json = {
#     "name": "record_user_details",
#     "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "email": {
#                 "type": "string",
#                 "description": "The email address of this user"
#             },
#             "name": {
#                 "type": "string",
#                 "description": "The user's name, if they provided it"
#             }
#             ,
#             "notes": {
#                 "type": "string",
#                 "description": "Any additional information about the conversation that's worth recording to give context"
#             }
#         },
#         "required": ["email"],
#         "additionalProperties": False
#     }
# }

# record_unknown_question_json = {
#     "name": "record_unknown_question",
#     "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
#     "parameters": {
#         "type": "object",
#         "properties": {
#             "question": {
#                 "type": "string",
#                 "description": "The question that couldn't be answered"
#             },
#         },
#         "required": ["question"],
#         "additionalProperties": False
#     }
# }

# tools = [{"type": "function", "function": record_user_details_json},
#         {"type": "function", "function": record_unknown_question_json}]


# class Me:

#     def __init__(self):
#         self.openai = OpenAI()
#         self.name = "Ed Donner"
#         reader = PdfReader("me/linkedin.pdf")
#         self.linkedin = ""
#         for page in reader.pages:
#             text = page.extract_text()
#             if text:
#                 self.linkedin += text
#         with open("me/summary.txt", "r", encoding="utf-8") as f:
#             self.summary = f.read()


#     def handle_tool_call(self, tool_calls):
#         results = []
#         for tool_call in tool_calls:
#             tool_name = tool_call.function.name
#             arguments = json.loads(tool_call.function.arguments)
#             print(f"Tool called: {tool_name}", flush=True)
#             tool = globals().get(tool_name)
#             result = tool(**arguments) if tool else {}
#             results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id})
#         return results
    
#     def system_prompt(self):
#         system_prompt = f"You are acting as {self.name}. You are answering questions on {self.name}'s website, \
# particularly questions related to {self.name}'s career, background, skills and experience. \
# Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. \
# You are given a summary of {self.name}'s background and LinkedIn profile 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 you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \
# If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. "

#         system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
#         system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
#         return system_prompt
    
#     def chat(self, message, history):
#         messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
#         done = False
#         while not done:
#             response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
#             if response.choices[0].finish_reason=="tool_calls":
#                 message = response.choices[0].message
#                 tool_calls = message.tool_calls
#                 results = self.handle_tool_call(tool_calls)
#                 messages.append(message)
#                 messages.extend(results)
#             else:
#                 done = True
#         return response.choices[0].message.content
    

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


from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr

load_dotenv(override=True)

GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
google_api_key = os.getenv("GOOGLE_API_KEY")

# Initialize Gemini client
gemini = OpenAI(
    base_url=GEMINI_BASE_URL,
    api_key=google_api_key
)

def push(text):
    requests.post(
        "https://api.pushover.net/1/messages.json",
        data={
            "token": os.getenv("PUSHOVER_TOKEN"),
            "user": os.getenv("PUSHOVER_USER"),
            "message": text,
        }
    )


def record_user_details(email, name="Name not provided", notes="not provided"):
    push(f"Recording {name} with email {email} and notes {notes}")
    return {"recorded": "ok"}


def record_unknown_question(question):
    push(f"Recording {question}")
    return {"recorded": "ok"}


record_user_details_json = {
    "name": "record_user_details",
    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {
                "type": "string",
                "description": "The email address of this user"
            },
            "name": {
                "type": "string",
                "description": "The user's name, if they provided it"
            },
            "notes": {
                "type": "string",
                "description": "Any additional information about the conversation that's worth recording to give context"
            }
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {
                "type": "string",
                "description": "The question that couldn't be answered"
            },
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

tools = [
    {"type": "function", "function": record_user_details_json},
    {"type": "function", "function": record_unknown_question_json}
]


class Me:

    def __init__(self):
        self.openai = gemini     # REPLACED OpenAI WITH GEMINI
        self.name = "AKASH M J"

        reader = PdfReader("me/Profile.pdf")
        self.linkedin = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.linkedin += text

        with open("me/summary.txt", "r", encoding="utf-8") as f:
            self.summary = f.read()

    def handle_tool_call(self, tool_calls):
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
            print(f"Tool called: {tool_name}", flush=True)
            tool = globals().get(tool_name)
            result = tool(**arguments) if tool else {}
            results.append({
                "role": "tool",
                "content": json.dumps(result),
                "tool_call_id": tool_call.id
            })
        return results

    def system_prompt(self):
        system_prompt = (
            f"You are acting as {self.name}. You are answering questions on {self.name}'s website, "
            f"particularly questions related to {self.name}'s career, background, skills and experience. "
            f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. "
            f"You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. "
            f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. "
            f"If you don't know the answer to any question, use your record_unknown_question tool to record the question. "
            f"If the user is engaging in discussion, try to steer them towards getting in touch via email."
        )

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

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

        done = False
        while not done:
            # ---- CHANGED TO USE GEMINI ----
            response = self.openai.chat.completions.create(
                model="gemini-2.5-flash",
                messages=messages,
                tools=tools
            )
            # --------------------------------

            if response.choices[0].finish_reason == "tool_calls":
                message = response.choices[0].message
                tool_calls = message.tool_calls
                results = self.handle_tool_call(tool_calls)
                messages.append(message)
                messages.extend(results)
            else:
                done = True

        return response.choices[0].message.content


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