app.py

# app.py — NVIDIA NIM + Tool Calling + Gradio Chatbot
# Model: meta/llama3-8b-instruct (supports OpenAI-style tools)
# Works with your nvapi-xxxx key on HuggingFace Spaces.

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

# ===============================
# ENVIRONMENT CONFIG
# ===============================
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")   # your nvapi-xxxx key
BASE_URL = "https://integrate.api.nvidia.com/v1"
MODEL = "meta/llama3-8b-instruct"

PUSHOVER_TOKEN = os.environ.get("PUSHOVER_TOKEN")
PUSHOVER_USER = os.environ.get("PUSHOVER_USER")

client = OpenAI(api_key=OPENAI_API_KEY, base_url=BASE_URL)


# ===============================
# OPTIONAL: Pushover notification
# ===============================
def push(text):
    try:
        if not PUSHOVER_TOKEN or not PUSHOVER_USER:
            print("Pushover not configured:", text)
            return
        requests.post(
            "https://api.pushover.net/1/messages.json",
            data={
                "token": PUSHOVER_TOKEN,
                "user": PUSHOVER_USER,
                "message": text,
            },
            timeout=10
        )
    except Exception as e:
        print("Pushover failed:", e)


# ===============================
# TOOL IMPLEMENTATIONS
# ===============================
def record_user_details(email, name="Name not provided", notes="not provided"):
    push(f"Lead captured → {name} | {email} | Notes: {notes}")
    return {"status": "ok", "email": email, "name": name, "notes": notes}

def record_unknown_question(question):
    push(f"Unknown question: {question}")
    return {"status": "ok", "question": question}


# Register tools globally
globals()["record_user_details"] = record_user_details
globals()["record_unknown_question"] = record_unknown_question


# ===============================
# TOOL JSON DEFINITIONS
# ===============================
tools = [
    {
        "type": "function",
        "function": {
            "name": "record_user_details",
            "description": "Record user's interest and email.",
            "parameters": {
                "type": "object",
                "properties": {
                    "email": {"type": "string"},
                    "name": {"type": "string"},
                    "notes": {"type": "string"},
                },
                "required": ["email"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "record_unknown_question",
            "description": "Record any question the assistant cannot answer.",
            "parameters": {
                "type": "object",
                "properties": {
                    "question": {"type": "string"},
                },
                "required": ["question"]
            }
        }
    }
]


# ===============================
# MAIN ASSISTANT CLASS
# ===============================
class Me:
    def __init__(self):
        self.name = "Ayush Tyagi"
        self.summary = ""
        self.linkedin_text = ""

        # Load summary file
        if os.path.exists("me/summary.txt"):
            self.summary = open("me/summary.txt", "r", encoding="utf-8").read()

        # Load PDF if exists
        pdf_path = "me/Ayush_linkdin.pdf"
        if os.path.exists(pdf_path):
            text = []
            reader = PdfReader(pdf_path)
            for page in reader.pages:
                page_text = page.extract_text()
                if page_text:
                    text.append(page_text)
            self.linkedin_text = "\n\n".join(text)

    def system_prompt(self):
        return f"""
You are acting as {self.name}. You answer questions about his background,
skills, experience, portfolio, and career.

If you DO NOT know something:
➡ Use the tool: record_unknown_question

If the user is interested in working with Ayush:
➡ Ask for their email and call: record_user_details

Be friendly, confident, and professional.

### Summary:
{self.summary}

### LinkedIn Extract:
{self.linkedin_text}
"""

    # ===============================
    # CHAT LOOP WITH TOOL CALLING
    # ===============================
    def chat(self, message, history):
        messages = [{"role": "system", "content": self.system_prompt()}]

        # FIXED: Handle both tuple-history & dict-history
        for item in history:
            if isinstance(item, (list, tuple)) and len(item) == 2:
                user_msg, bot_msg = item
                if user_msg:
                    messages.append({"role": "user", "content": user_msg})
                if bot_msg:
                    messages.append({"role": "assistant", "content": bot_msg})

            elif isinstance(item, dict) and "role" in item and "content" in item:
                messages.append({"role": item["role"], "content": item["content"]})

        messages.append({"role": "user", "content": message})

        while True:
            response = client.chat.completions.create(
                model=MODEL,
                messages=messages,
                tools=tools,
                tool_choice="auto",
                max_tokens=600
            )

            choice = response.choices[0]
            finish = choice.finish_reason
            msg = choice.message

            # ---- TOOL CALL ----
            if finish == "tool_calls":
                messages.append(msg.dict())

                for tool_call in msg.tool_calls:
                    func = tool_call.function
                    name = func.name
                    args = json.loads(func.arguments)

                    tool_fn = globals().get(name)
                    result = tool_fn(**args)

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

                # Let model continue after tool execution
                continue

            # ---- NORMAL RESPONSE ----
            return msg.content


# ===============================
# GRADIO APP
# ===============================
me = Me()

ui = gr.ChatInterface(
    fn=me.chat,
    title="Ayush Tyagi — Personal Assistant",
    type="messages"
)

if __name__ == "__main__":
    ui.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))