from dotenv import load_dotenv from openai import OpenAI import json import os import requests from pypdf import PdfReader from pathlib import Path import gradio as gr import time load_dotenv(override=True) def push(text): requests.post( "https://api.pushover.net/1/messages.json", data={ "token": os.getenv("PUSHOVER_TOKEN"), "user": os.getenv("PUSHOVER_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}] def load_pdf_with_cache(pdf_path: str, cache_dir="me/cache") -> str: pdf_path = Path(pdf_path) cache_dir = Path(cache_dir) cache_dir.mkdir(parents=True, exist_ok=True) cache_file = cache_dir / f"{pdf_path.stem}.txt" # Use cached text if available if cache_file.exists(): with open(cache_file, "r", encoding="utf-8") as f: return f.read() # Otherwise parse PDF (slow path) reader = PdfReader(str(pdf_path)) text = "" for page in reader.pages: page_text = page.extract_text() if page_text: text += page_text + "\n" # Save cache for future cold starts with open(cache_file, "w", encoding="utf-8") as f: f.write(text) return text class Me: def __init__(self): self.openai = OpenAI() # self.gemini = OpenAI(api_key=os.getenv("GOOGLE_API_KEY"), base_url="https://generativelanguage.googleapis.com/v1beta/openai/") # self.groq = OpenAI(api_key=os.getenv("GROQ_API_KEY"), base_url="https://api.groq.com/openai/v1") # self.groq_model_name = "openai/gpt-oss-120b" self.cur_model = 'gpt' self.name = "Mohit Kumar" self.linkedin = load_pdf_with_cache("me/linkedin.pdf") # reader = PdfReader("me/linkedin.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() # reader = PdfReader("me/mkt_v1_2pg.pdf") # self.resume = "" # for page in reader.pages: # text = page.extract_text() # if text: # self.resume += text self.resume = load_pdf_with_cache("me/mkt_v1_2pg.pdf") print("Linkedin and resume loaded successfully.", flush=True) self._system_prompt = self.system_prompt() def stream_llm(self, messages): """ Tries Groq streaming first, falls back to OpenAI streaming """ if self.cur_model == 'groq': try: return self.groq.chat.completions.create( model=self.groq_model_name, messages=messages, # tools=tools, stream=True, ) except Exception as e: print("Groq streaming failed:", e, flush=True) return self.openai.chat.completions.create( model="gpt-5-nano", messages=messages, # tools=tools, stream=True, ) else: print("self.cur_model changed. Using gpt nano for streaming.", flush=True) return self.openai.chat.completions.create( model="gpt-5-nano", messages=messages, # tools=tools, stream=True, ) 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. \ Do not be too pushy about getting in touch via email. my email id is strictly mohit.in@outlook.com, do not use any other email id. You can provide my linkedin profile url as a contact option along with my email id. \ Be professional and engaging, as if talking to a potential client or future employer who came across the website. Answer in a concise and to the point manner." system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n## Resume:\n{self.resume}\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 + [{"role": "user", "content": message}] # if not history: # # messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] # messages = [{"role": "system", "content": self.system_prompt()}] # else: # messages = [] # messages += history # messages.append({"role": "user", "content": message}) done = False while not done: response = self.openai.chat.completions.create(model="gpt-5-nano", messages=messages, tools=tools) # try: # # response = self.gemini.chat.completions.create(model="gemini-2.5-flash", messages=messages, tools=tools) # response = self.groq.chat.completions.create(model=self.groq_model_name, messages=messages, tools=tools) # print("Groq successful") # except Exception as e: # print("Groq failed:", e, flush=True) # self.cur_model = 'openai' # response = self.openai.chat.completions.create(model="gpt-5-nano", messages=messages, tools=tools) # print("GPT 5 nano successful.", flush=True) if response.choices[0].finish_reason=="tool_calls": message = response.choices[0].message tool_calls = message.tool_calls results = self.handle_tool_call(tool_calls) messages.append(message) messages.extend(results) else: done = True return response.choices[0].message.content # stream = self.stream_llm(messages) # partial = "" # for chunk in stream: # delta = chunk.choices[0].delta # if delta and delta.content: # partial += delta.content # yield partial # time.sleep(0.01) if __name__ == "__main__": me = Me() gr.ChatInterface(me.chat, type="messages").launch()