app.py

from pydoc import cli
from dotenv import load_dotenv
from openai import OpenAI
from datetime import datetime
from zoneinfo import ZoneInfo
from requests.exceptions import RequestException, Timeout
import json
import requests
from pypdf import PdfReader
import gradio as gr
import os


load_dotenv(override=True)

def push(text: str) -> bool:
    if not text or not isinstance(text, str):
        raise ValueError("`text` must be a non-empty string")

    ts = datetime.now(ZoneInfo("Asia/Kolkata")).isoformat()

    payload = {
        "timestamp": ts,
        "message": text,
    }

    headers = {
        "Content-Type": "application/json",
        "Authorization": "3175641f-82e8-4243tinytools8e62-4a202127c2db"
    }

    try:
        response = requests.post(
            "https://converteasly.com/api/send-feedback",
            json=payload,
            headers=headers,
            timeout=20
        )

        response.raise_for_status()

        return True

    except Timeout:
        print("❌ Request timed out while sending feedback")
        return False

    except RequestException as e:
        print(f"❌ Failed to send feedback: {e}")
        return False

    except Exception as e:
        print(f"❌ Unexpected error: {e}")
        return False



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

def record_unknown_question(question):
    print(f"Recording {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 = "Pawan Malhotra"
        reader = PdfReader("me/my-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, \
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[-6:],  # 🔥 limit history to last N turns
            {"role": "user", "content": message}
        ]

        def run(client, model):
            if client is open_router_client:
                return client.chat.completions.create(
                    model=model,
                    messages=messages,
                    tools=tools
                )
            else:
                return client.chat.completions.create(
                    model=model,
                    messages=messages,
                    tools=tools,
                    max_tokens=512  # 🔒 hard cap
                )

        # Clients
        gemini = OpenAI(
            api_key=os.getenv("GOOGLE_API_KEY"),
            base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
        )
        openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        open_router_client = OpenAI(
            api_key=os.getenv("OPEN_ROUTER_API_KEY"),
            base_url="https://openrouter.ai/api/v1"
        )

        try:
            response = run(gemini, "gemini-2.0-flash")
        except Exception as e:
            if "quota" in str(e).lower() or "resource_exhausted" in str(e).lower():
                print("Google Limit Exceeded! Falling to open API")
                try:
                    response = run(openai_client, "gpt-4o-mini")
                except Exception as gpt:
                    if "rate limit" in str(gpt).lower() or "rate_limit_exceeded" in str(gpt).lower():
                        print("Open API rate limit exceeded! Falling to Open Router API")
                        # xiaomi/mimo-v2-flash:free
                        # allenai/olmo-3.1-32b-think:free
                        response = run(open_router_client, "xiaomi/mimo-v2-flash:free")
                    else:
                        raise
            else:
                raise

        choice = response.choices[0]

        # 🔁 Handle tools ONCE (no loops)
        if choice.finish_reason == "tool_calls":
            tool_results = self.handle_tool_call(choice.message.tool_calls)

            messages.append(choice.message)
            messages.extend(tool_results)

            # Final answer (NO tools this time)
            response = run(open_router_client, "xiaomi/mimo-v2-flash:free")
        return response.choices[0].message.content



if __name__ == "__main__":
    me = Me()
    with gr.Blocks(
        theme=gr.themes.Soft(),
        css="""
        .gradio-container {
            width: 100%;
            margin: auto;
        }
        footer {display: none !important;}
        """
    ) as demo:
        gr.Markdown(
            """
            # 🧠 Virtual Resume Assistant - Pawan Malhotra
            Ask anything about my skills, experience, or projects.
            """
        )

        gr.ChatInterface(
            fn=me.chat,
            type="messages",
            # chatbot=gr.Chatbot(
            #     height=450,
            #     show_copy_button=True,
            #     avatar_images=("👤", "🤖")
            # ),
            textbox=gr.Textbox(
                placeholder="Ask about experience, skills, projects...",
                scale=7
            ),
        )
    demo.launch()