Spaces:
Sleeping
Sleeping
| 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 = "" | |
| 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() | |