from dotenv import load_dotenv from openai import OpenAI import json import logging import os import requests import traceback import warnings from pathlib import Path from pypdf import PdfReader import gradio as gr warnings.filterwarnings("ignore", category=UserWarning, module="pypdf") logging.getLogger("pypdf").setLevel(logging.ERROR) load_dotenv(override=True) def push(text): token = os.getenv("PUSHOVER_TOKEN") user = os.getenv("PUSHOVER_USER") if not token or not user: print("Pushover not configured; skipping notification", flush=True) return requests.post( "https://api.pushover.net/1/messages.json", data={"token": token, "user": 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.name = "Hamdalla Shawahny" self._openai = None self._load_me_files() def _client(self): """Do not create OpenAI() at import time — Spaces need the UI to start without a key.""" if self._openai is None: if not os.getenv("OPENAI_API_KEY"): return None self._openai = OpenAI() return self._openai def _load_me_files(self): me_dir = Path(__file__).resolve().parent / "me" self.linkedin = "" pdf_path = me_dir / "linkedin.pdf" if pdf_path.is_file() and pdf_path.stat().st_size > 0: try: reader = PdfReader(pdf_path, strict=False) for page in reader.pages: text = page.extract_text() if text: self.linkedin += text except Exception as e: print(f"linkedin.pdf skipped ({e})", flush=True) fallback = me_dir / "linkedin_profile.txt" if not self.linkedin.strip() and fallback.is_file(): self.linkedin = fallback.read_text(encoding="utf-8") summary_path = me_dir / "summary.txt" if summary_path.is_file(): self.summary = summary_path.read_text(encoding="utf-8") else: self.summary = "" @staticmethod def _history_for_api(history): out = [] for m in history: if not isinstance(m, dict): continue role = m.get("role") if role not in ("user", "assistant", "system", "tool"): continue item = {"role": role, "content": m.get("content") or ""} if role == "tool" and m.get("tool_call_id"): item["tool_call_id"] = m["tool_call_id"] if role == "assistant" and m.get("tool_calls"): item["tool_calls"] = m["tool_calls"] out.append(item) return out def handle_tool_call(self, tool_calls): results = [] for tool_call in tool_calls: tool_name = tool_call.function.name try: arguments = json.loads(tool_call.function.arguments or "{}") except json.JSONDecodeError: 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): try: client = self._client() if client is None: return ( "**OpenAI API key is missing.** On Hugging Face: open your Space → **Settings** → " "**Variables and secrets** → add a secret named exactly `OPENAI_API_KEY` with your key " "from [OpenAI API keys](https://platform.openai.com/api-keys), then **Restart** the Space." ) self._load_me_files() history = self._history_for_api(history) messages = [ {"role": "system", "content": self.system_prompt()}, *history, {"role": "user", "content": message or ""}, ] done = False while not done: response = client.chat.completions.create( model="gpt-4o-mini", messages=messages, tools=tools ) choice = response.choices[0] if choice.finish_reason == "tool_calls" and choice.message.tool_calls: assistant_message = choice.message results = self.handle_tool_call(assistant_message.tool_calls) messages.append(assistant_message) messages.extend(results) else: done = True content = response.choices[0].message.content return content if content else "" except Exception as e: traceback.print_exc() return ( f"**Error:** `{type(e).__name__}: {e}`\n\n" "See Space **Logs** for the full traceback." ) if __name__ == "__main__": me = Me() gr.ChatInterface(me.chat, type="messages").launch()