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| """ | |
| Personal Website Chatbot Application | |
| This application creates an AI-powered chatbot that impersonates the owner using their | |
| LinkedIn profile and personal summary. It uses OpenAI's GPT model with function calling | |
| to handle conversations and capture leads through Pushover notifications. | |
| """ | |
| # Import required libraries | |
| from dotenv import load_dotenv # For loading environment variables | |
| from openai import OpenAI # OpenAI API client | |
| import json # JSON processing for function calls | |
| import os # Operating system interface | |
| import requests # HTTP requests for Pushover notifications | |
| from pypdf import PdfReader # PDF text extraction | |
| import gradio as gr # Web interface framework | |
| # Load environment variables from .env file | |
| load_dotenv(override=True) | |
| # ================================ | |
| # NOTIFICATION FUNCTIONS | |
| # ================================ | |
| def push(text): | |
| """ | |
| Send a notification via Pushover API. | |
| Args: | |
| text (str): The message text to send | |
| """ | |
| requests.post( | |
| "https://api.pushover.net/1/messages.json", | |
| data={ | |
| "token": os.getenv("PUSHOVER_TOKEN"), | |
| "user": os.getenv("PUSHOVER_USER"), | |
| "message": text, | |
| } | |
| ) | |
| # ================================ | |
| # TOOL FUNCTIONS FOR OPENAI | |
| # ================================ | |
| def record_user_details(email, name="Name not provided", notes="not provided"): | |
| """ | |
| Record user contact details and send notification. | |
| This function is called by OpenAI when a user provides their contact information. | |
| Args: | |
| email (str): User's email address (required) | |
| name (str): User's name (optional) | |
| notes (str): Additional conversation context (optional) | |
| Returns: | |
| dict: Success confirmation for OpenAI | |
| """ | |
| push(f"Recording {name} with email {email} and notes {notes}") | |
| return {"recorded": "ok"} | |
| def record_unknown_question(question): | |
| """ | |
| Record questions the chatbot couldn't answer. | |
| This function is called by OpenAI when encountering unknown questions | |
| to help improve the chatbot's knowledge base. | |
| Args: | |
| question (str): The question that couldn't be answered | |
| Returns: | |
| dict: Success confirmation for OpenAI | |
| """ | |
| push(f"Recording {question}") | |
| return {"recorded": "ok"} | |
| # ================================ | |
| # OPENAI FUNCTION SCHEMAS | |
| # ================================ | |
| # Schema definition for the user details recording function | |
| 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 | |
| } | |
| } | |
| # Schema definition for the unknown question recording function | |
| 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 | |
| } | |
| } | |
| # Combined tools list for OpenAI function calling | |
| tools = [ | |
| {"type": "function", "function": record_user_details_json}, | |
| {"type": "function", "function": record_unknown_question_json} | |
| ] | |
| # ================================ | |
| # MAIN CHATBOT CLASS | |
| # ================================ | |
| class Me: | |
| """ | |
| Main chatbot class that handles conversations and impersonates the owner. | |
| This class loads profile data, manages OpenAI interactions, and handles | |
| function calling for lead capture and question tracking. | |
| """ | |
| def __init__(self): | |
| """ | |
| Initialize the chatbot with profile data and OpenAI client. | |
| Loads LinkedIn PDF and summary text file to create the chatbot's | |
| knowledge base about the owner. | |
| """ | |
| # Initialize OpenAI client | |
| self.openai = OpenAI() | |
| # Set the owner's name (hardcoded for now) | |
| self.name = "Gowrisankar" | |
| # Load LinkedIn profile from PDF | |
| reader = PdfReader("documents/linkedin.pdf") | |
| self.linkedin = "" | |
| for page in reader.pages: | |
| text = page.extract_text() | |
| if text: | |
| self.linkedin += text | |
| # Load personal summary from text file | |
| with open("documents/summary.txt", "r", encoding="utf-8") as f: | |
| self.summary = f.read() | |
| def handle_tool_call(self, tool_calls): | |
| """ | |
| Execute OpenAI function calls and return results. | |
| This method processes tool calls from OpenAI, executes the corresponding | |
| Python functions, and formats the results for the conversation. | |
| Args: | |
| tool_calls: List of tool calls from OpenAI response | |
| Returns: | |
| list: Formatted tool results for OpenAI conversation | |
| """ | |
| results = [] | |
| for tool_call in tool_calls: | |
| # Extract tool name and arguments | |
| tool_name = tool_call.function.name | |
| arguments = json.loads(tool_call.function.arguments) | |
| print(f"Tool called: {tool_name}", flush=True) | |
| # Get the corresponding Python function and execute it | |
| tool = globals().get(tool_name) | |
| result = tool(**arguments) if tool else {} | |
| # Format result for OpenAI | |
| results.append({ | |
| "role": "tool", | |
| "content": json.dumps(result), | |
| "tool_call_id": tool_call.id | |
| }) | |
| return results | |
| def system_prompt(self): | |
| """ | |
| Generate the system prompt with owner's profile data. | |
| Creates a comprehensive system prompt that instructs the AI to impersonate | |
| the owner using their LinkedIn profile and summary information. | |
| Returns: | |
| str: Complete system prompt for OpenAI | |
| """ | |
| 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.""" | |
| # Add profile data to the prompt | |
| 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): | |
| """ | |
| Handle a chat message and return the AI response. | |
| This method implements the conversation loop with OpenAI, handling | |
| function calls and continuing the conversation until a final response. | |
| Args: | |
| message (str): User's input message | |
| history (list): Previous conversation history | |
| Returns: | |
| str: AI's response message | |
| """ | |
| # Build complete message history including system prompt | |
| messages = [ | |
| {"role": "system", "content": self.system_prompt()} | |
| ] + history + [ | |
| {"role": "user", "content": message} | |
| ] | |
| # Continue conversation until no more function calls | |
| done = False | |
| while not done: | |
| # Get response from OpenAI | |
| response = self.openai.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| tools=tools | |
| ) | |
| # Check if OpenAI wants to call functions | |
| if response.choices[0].finish_reason == "tool_calls": | |
| # Extract and execute function calls | |
| message_with_tools = response.choices[0].message | |
| tool_calls = message_with_tools.tool_calls | |
| tool_results = self.handle_tool_call(tool_calls) | |
| # Add function call and results to conversation | |
| messages.append(message_with_tools) | |
| messages.extend(tool_results) | |
| else: | |
| # No more function calls, conversation is complete | |
| done = True | |
| return response.choices[0].message.content | |
| # ================================ | |
| # APPLICATION ENTRY POINT | |
| # ================================ | |
| if __name__ == "__main__": | |
| # Initialize the chatbot | |
| me = Me() | |
| # Create and launch the Gradio web interface | |
| # type="messages" enables proper conversation history handling | |
| gr.ChatInterface(me.chat, type="messages", cache_examples=False).launch() | |