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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 | |
| import base64 | |
| import time | |
| from collections import defaultdict | |
| import fastapi | |
| from gradio.context import Context | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| logger.setLevel(logging.DEBUG) | |
| load_dotenv(override=True) | |
| class RateLimiter: | |
| def __init__(self, max_requests=5, time_window=5): | |
| # max_requests per time_window seconds | |
| self.max_requests = max_requests | |
| self.time_window = time_window # in seconds | |
| self.request_history = defaultdict(list) | |
| def is_rate_limited(self, user_id): | |
| current_time = time.time() | |
| # Remove old requests | |
| self.request_history[user_id] = [ | |
| timestamp for timestamp in self.request_history[user_id] | |
| if current_time - timestamp < self.time_window | |
| ] | |
| # Check if user has exceeded the limit | |
| if len(self.request_history[user_id]) >= self.max_requests: | |
| return True | |
| # Add current request | |
| self.request_history[user_id].append(current_time) | |
| return False | |
| 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 send_email(from_email, name, notes): | |
| auth = base64.b64encode(f'api:{os.getenv("MAILGUN_API_KEY")}'.encode()).decode() | |
| response = requests.post( | |
| f'https://api.mailgun.net/v3/{os.getenv("MAILGUN_DOMAIN")}/messages', | |
| headers={ | |
| 'Authorization': f'Basic {auth}' | |
| }, | |
| data={ | |
| 'from': f'Website Contact <mailgun@{os.getenv("MAILGUN_DOMAIN")}>', | |
| 'to': os.getenv("MAILGUN_RECIPIENT"), | |
| 'subject': f'New message from {from_email}', | |
| 'text': f'Name: {name}\nEmail: {from_email}\nNotes: {notes}', | |
| 'h:Reply-To': from_email | |
| } | |
| ) | |
| return response.status_code == 200 | |
| def record_user_details(email, name="Name not provided", notes="not provided"): | |
| push(f"Recording {name} with email {email} and notes {notes}") | |
| # Send email notification | |
| email_sent = send_email(email, name, notes) | |
| return {"recorded": "ok", "email_sent": email_sent} | |
| 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(api_key=os.getenv("GOOGLE_API_KEY"), base_url="https://generativelanguage.googleapis.com/v1beta/openai/") | |
| self.name = "Sagarnil Das" | |
| self.rate_limiter = RateLimiter(max_requests=5, time_window=60) # 5 messages per minute | |
| 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, \ | |
| 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. \ | |
| When a user provides their email, both a push notification and an email notification will be sent. If the user does not provide any note in the message \ | |
| in which they provide their email, then give a summary of the conversation so far as the notes." | |
| 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): | |
| # Get the client IP from Gradio's request context | |
| try: | |
| # Try to get the real client IP from request headers | |
| request = Context.get_context().request | |
| # Check for X-Forwarded-For header (common in reverse proxies like HF Spaces) | |
| forwarded_for = request.headers.get("X-Forwarded-For") | |
| # Check for Cf-Connecting-IP header (Cloudflare) | |
| cloudflare_ip = request.headers.get("Cf-Connecting-IP") | |
| if forwarded_for: | |
| # X-Forwarded-For contains a comma-separated list of IPs, the first one is the client | |
| user_id = forwarded_for.split(",")[0].strip() | |
| elif cloudflare_ip: | |
| user_id = cloudflare_ip | |
| else: | |
| # Fall back to direct client address | |
| user_id = request.client.host | |
| except (AttributeError, RuntimeError, fastapi.exceptions.FastAPIError): | |
| # Fallback if we can't get context or if running outside of FastAPI | |
| user_id = "default_user" | |
| logger.debug(f"User ID: {user_id}") | |
| if self.rate_limiter.is_rate_limited(user_id): | |
| return "You're sending messages too quickly. Please wait a moment before sending another message." | |
| messages = [{"role": "system", "content": self.system_prompt()}] | |
| # Check if history is a list of dicts (Gradio "messages" format) | |
| if isinstance(history, list) and all(isinstance(h, dict) for h in history): | |
| messages.extend(history) | |
| else: | |
| # Assume it's a list of [user_msg, assistant_msg] pairs | |
| for user_msg, assistant_msg in history: | |
| messages.append({"role": "user", "content": user_msg}) | |
| messages.append({"role": "assistant", "content": assistant_msg}) | |
| messages.append({"role": "user", "content": message}) | |
| done = False | |
| while not done: | |
| response = self.openai.chat.completions.create( | |
| model="gemini-2.0-flash", | |
| messages=messages, | |
| tools=tools | |
| ) | |
| if response.choices[0].finish_reason == "tool_calls": | |
| tool_calls = response.choices[0].message.tool_calls | |
| tool_result = self.handle_tool_call(tool_calls) | |
| messages.append(response.choices[0].message) | |
| messages.extend(tool_result) | |
| else: | |
| done = True | |
| return response.choices[0].message.content | |
| if __name__ == "__main__": | |
| me = Me() | |
| gr.ChatInterface(me.chat, type="messages").launch() | |