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| from pydoc import cli | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| from datetime import datetime | |
| from zoneinfo import ZoneInfo | |
| from requests.exceptions import RequestException, Timeout | |
| import json | |
| import requests | |
| from pypdf import PdfReader | |
| import gradio as gr | |
| import os | |
| load_dotenv(override=True) | |
| def push(text: str) -> bool: | |
| if not text or not isinstance(text, str): | |
| raise ValueError("`text` must be a non-empty string") | |
| ts = datetime.now(ZoneInfo("Asia/Kolkata")).isoformat() | |
| payload = { | |
| "timestamp": ts, | |
| "message": text, | |
| } | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": "3175641f-82e8-4243tinytools8e62-4a202127c2db" | |
| } | |
| try: | |
| response = requests.post( | |
| "https://converteasly.com/api/send-feedback", | |
| json=payload, | |
| headers=headers, | |
| timeout=20 | |
| ) | |
| response.raise_for_status() | |
| return True | |
| except Timeout: | |
| print("❌ Request timed out while sending feedback") | |
| return False | |
| except RequestException as e: | |
| print(f"❌ Failed to send feedback: {e}") | |
| return False | |
| except Exception as e: | |
| print(f"❌ Unexpected error: {e}") | |
| return False | |
| def record_user_details(email, name="Name not provided", notes="not provided"): | |
| print(f"Recording {name} with email {email} and notes {notes}") | |
| push(f"Recording {name} with email {email} and notes {notes}") | |
| return {"recorded": "ok"} | |
| def record_unknown_question(question): | |
| print(f"Recording {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 = "Pawan Malhotra" | |
| reader = PdfReader("me/my-profile.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. " | |
| 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): | |
| messages = [ | |
| {"role": "system", "content": self.system_prompt()}, | |
| *history[-6:], # 🔥 limit history to last N turns | |
| {"role": "user", "content": message} | |
| ] | |
| def run(client, model): | |
| if client is open_router_client: | |
| return client.chat.completions.create( | |
| model=model, | |
| messages=messages, | |
| tools=tools | |
| ) | |
| else: | |
| return client.chat.completions.create( | |
| model=model, | |
| messages=messages, | |
| tools=tools, | |
| max_tokens=512 # 🔒 hard cap | |
| ) | |
| # Clients | |
| gemini = OpenAI( | |
| api_key=os.getenv("GOOGLE_API_KEY"), | |
| base_url="https://generativelanguage.googleapis.com/v1beta/openai/" | |
| ) | |
| openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) | |
| open_router_client = OpenAI( | |
| api_key=os.getenv("OPEN_ROUTER_API_KEY"), | |
| base_url="https://openrouter.ai/api/v1" | |
| ) | |
| try: | |
| response = run(gemini, "gemini-2.0-flash") | |
| except Exception as e: | |
| if "quota" in str(e).lower() or "resource_exhausted" in str(e).lower(): | |
| print("Google Limit Exceeded! Falling to open API") | |
| try: | |
| response = run(openai_client, "gpt-4o-mini") | |
| except Exception as gpt: | |
| if "rate limit" in str(gpt).lower() or "rate_limit_exceeded" in str(gpt).lower(): | |
| print("Open API rate limit exceeded! Falling to Open Router API") | |
| # xiaomi/mimo-v2-flash:free | |
| # allenai/olmo-3.1-32b-think:free | |
| response = run(open_router_client, "xiaomi/mimo-v2-flash:free") | |
| else: | |
| raise | |
| else: | |
| raise | |
| choice = response.choices[0] | |
| # 🔁 Handle tools ONCE (no loops) | |
| if choice.finish_reason == "tool_calls": | |
| tool_results = self.handle_tool_call(choice.message.tool_calls) | |
| messages.append(choice.message) | |
| messages.extend(tool_results) | |
| # Final answer (NO tools this time) | |
| response = run(open_router_client, "xiaomi/mimo-v2-flash:free") | |
| return response.choices[0].message.content | |
| if __name__ == "__main__": | |
| me = Me() | |
| with gr.Blocks( | |
| theme=gr.themes.Soft(), | |
| css=""" | |
| .gradio-container { | |
| width: 100%; | |
| margin: auto; | |
| } | |
| footer {display: none !important;} | |
| """ | |
| ) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🧠 Virtual Resume Assistant - Pawan Malhotra | |
| Ask anything about my skills, experience, or projects. | |
| """ | |
| ) | |
| gr.ChatInterface( | |
| fn=me.chat, | |
| type="messages", | |
| # chatbot=gr.Chatbot( | |
| # height=450, | |
| # show_copy_button=True, | |
| # avatar_images=("👤", "🤖") | |
| # ), | |
| textbox=gr.Textbox( | |
| placeholder="Ask about experience, skills, projects...", | |
| scale=7 | |
| ), | |
| ) | |
| demo.launch() | |