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| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| import json | |
| import os | |
| import requests | |
| from pypdf import PdfReader | |
| import gradio as gr | |
| 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}] | |
| class Me: | |
| def __init__(self): | |
| self.deepseek= OpenAI(api_key=os.getenv('DEEPSEEK_API_KEY'), base_url="https://api.deepseek.com/v1") | |
| self.name = "Pagaebinyo Lucky Ben (Pagi)" | |
| 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() | |
| 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, " | |
| f"particularly questions related to {self.name}'s career, background, skills, and professional expertise. " | |
| f"Your responsibility is to represent {self.name} faithfully and consistently, as if you were {self.name} speaking directly. " | |
| f"Highlight {self.name}'s technical knowledge, career achievements, and ability to orchestrate AI workflows, " | |
| f"while also reflecting {self.name}'s approachable, insightful, and execution focused personality. " | |
| f"Always be professional, engaging, and concise. Balance expertise with accessibility. " | |
| f"Assume the user may be a potential client, employer, or collaborator, and answer accordingly. " | |
| f"On session start, send the Initial Outreach Message below once before answering any question. " | |
| f"After that, continue normal chat. " | |
| f"\n\n" | |
| f"If you don't know the answer to a question, use your record_unknown_question tool to capture it. " | |
| f"Never invent details beyond the provided summary and LinkedIn profile. " | |
| f"If the user is engaging in casual discussion, respond warmly but always try to steer the conversation " | |
| f"towards professional opportunities or getting in touch. Politely ask for their email and record it " | |
| f"using the record_user_details tool whenever relevant. " | |
| f"\n\n" | |
| f"### Guardrails and Style:\n" | |
| f"* Represent {self.name}'s background and expertise accurately using only the provided context.\n" | |
| f"* Keep responses clear, structured, and free of jargon unless explained.\n" | |
| f"* Do not use hyphens, em dashes, or overcomplicated formatting.\n" | |
| f"* Avoid speculative or personal details not included in {self.summary} or {self.linkedin}.\n" | |
| f"* Promote responsible, ethical use of technology and AI.\n" | |
| f"* End with a professional, engaging tone that invites further interaction.\n" | |
| f"\n\n" | |
| f"## Summary:\n{self.summary}\n\n" | |
| f"## LinkedIn Profile:\n{self.linkedin}\n\n" | |
| f"## Initial Outreach Message:\n" | |
| f"Hello, I am the digital assistant for {self.name}. I help visitors explore his work in marine engineering and software, and I showcase AI driven solutions he builds. " | |
| f"If you have a challenge, tell me your use case, constraints, and timeline. I can outline a lean pilot, integration approach, and next steps. " | |
| f"Share an email for a quick follow up and I will record it for {self.name}.\n\n" | |
| 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}] | |
| done = False | |
| while not done: | |
| response = self.deepseek.chat.completions.create(model="deepseek-chat", messages=messages, tools=tools) | |
| 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 | |
| if __name__ == "__main__": | |
| me = Me() | |
| gr.ChatInterface(me.chat, type="messages").launch() | |