app.py

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


load_dotenv(override=True)

def send_telegram_message(message):
    try:
        token = os.getenv("TELEGRAM_BOT_TOKEN")
        chat_id = os.getenv("TELEGRAM_CHAT_ID")

        if not token or not chat_id:
            print("Telegram token or chat_id missing", flush=True)
            return

        url = f"https://api.telegram.org/bot{token}/sendMessage"
        payload = {
            "chat_id": chat_id,
            "text": message,
        }
        response = requests.post(url, json=payload, timeout=10)
        print("📨 Telegram response:", response.status_code, response.text, flush=True)

    except Exception as e:
        print(f"Telegram error: {e}", flush=True)

def record_user_details(email, name="Name not provided", notes="not provided"):
    print("✅ record_user_details triggered", flush=True)
    send_telegram_message(f"Recording {name} with email {email} and notes {notes}")
    return {"recorded": "ok"}

def record_unknown_question(question):
    print("✅ record_unknown_question triggered", flush=True)
    send_telegram_message(f"Recording unknown question: {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 _resolve_cv_path(self):
        configured_path = os.getenv("CV_PATH")
        if configured_path:
            path = Path(configured_path)
            if not path.is_absolute():
                path = Path(__file__).parent / path
            if path.exists():
                return path

        me_dir = Path(__file__).parent / "me"
        pdf_files = sorted(me_dir.glob("*.pdf"), key=lambda p: p.stat().st_mtime, reverse=True)
        if not pdf_files:
            raise FileNotFoundError(f"No PDF found in {me_dir}")
        return pdf_files[0]

    def __init__(self):
        self.openai = OpenAI()
        self.name = "Venkata Vikranth Jannatha"
        cv_path = self._resolve_cv_path()
        print(f"Loading CV from: {cv_path}", flush=True)
        reader = PdfReader(str(cv_path))
        self.linkedin = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.linkedin += text
        self.summary = self.linkedin[:2000]


    def handle_tool_call(self, tool_calls):
        results = []
        for tool_call in tool_calls:
            try:

                tool_name = tool_call.function.name
                arguments = json.loads(tool_call.function.arguments)
                print(f"Tool Called : {tool_name}", flush=True)
                
                if tool_name == "record_user_details":
                    result = record_user_details(**arguments)
                elif tool_name == "record_unknown_question":
                    result = record_unknown_question(**arguments)
                else:
                    result = {}
                    print(f"Unknown tool called: {tool_name}", flush=True)
            except Exception as e:
                print(f"Error handling tool call: {e}", flush=True)
                result = {"error": str(e)}
            
            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 _normalize_history(self, history):
        """Convert Gradio chat history into OpenAI chat messages."""
        if not history:
            return []

        allowed_roles = {"system", "user", "assistant", "tool", "function", "developer"}

        # Newer Gradio can provide OpenAI-style message dicts already
        if isinstance(history, list) and history and isinstance(history[0], dict):
            normalized = []
            for item in history:
                role = item.get("role")
                content = item.get("content")
                if role in allowed_roles and isinstance(content, str):
                    normalized.append({"role": role, "content": content})
            return normalized

        # Legacy Gradio provides tuples: [(user_msg, assistant_msg), ...]
        normalized = []
        for pair in history:
            if not (isinstance(pair, (list, tuple)) and len(pair) == 2):
                continue
            user_msg, assistant_msg = pair
            if user_msg is not None and str(user_msg).strip() != "":
                normalized.append({"role": "user", "content": str(user_msg)})
            if assistant_msg is not None and str(assistant_msg).strip() != "":
                normalized.append({"role": "assistant", "content": str(assistant_msg)})
        return normalized
    
    def chat(self, message, history):
        try:
            history_messages = self._normalize_history(history)
            messages = [{"role": "system", "content": self.system_prompt()}] + history_messages + [{"role": "user", "content": message}]
            done = False
            while not done:
                response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools)
                finish_reason = response.choices[0].finish_reason
                print(finish_reason)
                
                if finish_reason == "tool_calls":
                    message_obj = response.choices[0].message
                    tool_calls = message_obj.tool_calls
                    results = self.handle_tool_call(tool_calls)
                    messages.append(message_obj.model_dump())
                    messages.extend(results)
                else:
                    done = True
            return response.choices[0].message.content
        except Exception as e:
            print(f"Error in chat: {e}", flush=True)
            return "Sorry, something went wrong while processing your message."
    

if __name__ == "__main__":
    me = Me()
    gr.ChatInterface(me.chat).launch(share=True)