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| import os | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| import gradio as gr | |
| from pydantic import BaseModel | |
| import requests | |
| import json | |
| # Load environment variables | |
| 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.openai = OpenAI() | |
| self.name = "Talha Umar" | |
| # Handle the case where the resume file might not exist | |
| try: | |
| with open("me/Resume.txt", "r", encoding="utf-8") as f: | |
| self.summary = f.read() | |
| except FileNotFoundError: | |
| self.summary = "Professional with experience in software development and technology." | |
| 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 experience 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}" | |
| system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}." | |
| return system_prompt | |
| def convert_gradio_history_to_openai_messages(self, history): | |
| """Convert Gradio history format to OpenAI messages format""" | |
| messages = [] | |
| for exchange in history: | |
| # Gradio history comes as [user_message, assistant_message] pairs | |
| if isinstance(exchange, list) and len(exchange) == 2: | |
| user_msg, assistant_msg = exchange | |
| if user_msg: | |
| messages.append({"role": "user", "content": user_msg}) | |
| if assistant_msg: | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| elif isinstance(exchange, dict): | |
| # If it's already in the right format | |
| messages.append(exchange) | |
| return messages | |
| def chat(self, message, history): | |
| # Convert Gradio history to OpenAI format | |
| openai_history = self.convert_gradio_history_to_openai_messages(history) | |
| # Build the complete message list | |
| messages = [{"role": "system", "content": self.system_prompt()}] + openai_history + [{"role": "user", "content": message}] | |
| done = False | |
| while not done: | |
| try: | |
| response = self.openai.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| tools=tools | |
| ) | |
| if response.choices[0].finish_reason == "tool_calls": | |
| message_obj = response.choices[0].message | |
| tool_calls = message_obj.tool_calls | |
| results = self.handle_tool_call(tool_calls) | |
| # Add the assistant message with tool calls | |
| messages.append({ | |
| "role": "assistant", | |
| "content": message_obj.content, | |
| "tool_calls": [ | |
| { | |
| "id": tc.id, | |
| "type": tc.type, | |
| "function": { | |
| "name": tc.function.name, | |
| "arguments": tc.function.arguments | |
| } | |
| } for tc in tool_calls | |
| ] | |
| }) | |
| # Add tool results | |
| messages.extend(results) | |
| else: | |
| done = True | |
| return response.choices[0].message.content | |
| except Exception as e: | |
| print(f"Error in chat: {e}") | |
| return f"I'm sorry, I encountered an error while processing your message. Please try again." | |
| return "Something went wrong. Please try again." | |
| if __name__ == "__main__": | |
| me = Me() | |
| # Create a custom theme that matches the portfolio design | |
| custom_theme = gr.themes.Soft( | |
| primary_hue="pink", | |
| secondary_hue="blue", | |
| neutral_hue="gray", | |
| font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"] | |
| ).set( | |
| body_background_fill="#ffffff", | |
| block_background_fill="#f7f7f8", | |
| block_border_width="0px", | |
| button_primary_background_fill="#ff69b4", | |
| button_primary_background_fill_dark="#ff1493", | |
| button_primary_text_color="white", | |
| block_radius="12px", | |
| block_title_text_weight="600", | |
| block_label_text_size="14px", | |
| block_label_margin="0.5rem" | |
| ) | |
| # Create the chat interface with custom styling | |
| chat_interface = gr.ChatInterface( | |
| fn=me.chat, | |
| title=f"Chat with {me.name}", | |
| description="Ask me anything about my experience, skills, or background!", | |
| theme=custom_theme, | |
| examples=[ | |
| "What's your background in web development?", | |
| "Tell me about your experience with UI/UX design", | |
| "What are your key skills?" | |
| ] | |
| ) | |
| # Remove share=True since it's not supported on Hugging Face Spaces | |
| chat_interface.launch() |