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from dotenv import load_dotenv |
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from openai import OpenAI |
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import datetime |
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import json |
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import os |
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import requests |
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from pypdf import PdfReader |
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import gradio as gr |
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import openmeteo_requests |
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load_dotenv(override=True) |
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def push(text): |
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requests.post( |
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"https://api.pushover.net/1/messages.json", |
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data={ |
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"token": os.getenv("PUSHOVER_TOKEN"), |
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"user": os.getenv("PUSHOVER_USER"), |
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"message": text, |
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} |
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) |
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openmeteo = openmeteo_requests.Client() |
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def get_weather(place_name:str, countryCode:str = ""): |
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coordinates = Geocoding().coordinates_search(place_name, countryCode) |
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if coordinates: |
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latitude = coordinates["results"][0]["latitude"] |
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longitude = coordinates["results"][0]["longitude"] |
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else: |
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return {"error": "No coordinates found"} |
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url = "https://api.open-meteo.com/v1/forecast" |
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params = { |
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"latitude": latitude, |
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"longitude": longitude, |
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"current": ["relative_humidity_2m", "temperature_2m", "apparent_temperature", "is_day", "precipitation", "cloud_cover", "wind_gusts_10m"], |
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"timezone": "auto", |
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"forecast_days": 1 |
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} |
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weather = openmeteo.weather_api(url, params=params) |
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current_weather = weather[0].Current() |
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current_time = current_weather.Time() |
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response = { |
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"current_relative_humidity_2m": current_weather.Variables(0).Value(), |
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"current_temperature_celcius": current_weather.Variables(1).Value(), |
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"current_apparent_temperature_celcius": current_weather.Variables(2).Value(), |
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"current_is_day": current_weather.Variables(3).Value(), |
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"current_precipitation": current_weather.Variables(4).Value(), |
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"current_cloud_cover": current_weather.Variables(5).Value(), |
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"current_wind_gusts": current_weather.Variables(6).Value(), |
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"current_time": current_time |
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} |
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return response |
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get_weather_json = { |
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"name": "get_weather", |
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"description": "Use this tool to get the weather at a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"place_name": { |
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"type": "string", |
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"description": "The name of the location to get the weather for (city or region name)" |
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}, |
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"countryCode": { |
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"type": "string", |
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"description": "The two-letter country code of the location" |
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} |
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}, |
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"required": ["place_name"], |
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"additionalProperties": False |
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} |
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} |
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def record_user_details(email, name="Name not provided", notes="not provided"): |
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push(f"Recording {name} with email {email} and notes {notes}") |
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return {"recorded": "ok"} |
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def record_unknown_question(question): |
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push(f"Recording {question}") |
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return {"recorded": "ok"} |
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record_user_details_json = { |
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"name": "record_user_details", |
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"description": "Use this tool to record that a user is interested in being in touch and provided an email address", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"email": { |
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"type": "string", |
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"description": "The email address of this user" |
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}, |
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"name": { |
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"type": "string", |
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"description": "The user's name, if they provided it" |
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} |
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, |
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"notes": { |
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"type": "string", |
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"description": "Any additional information about the conversation that's worth recording to give context" |
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} |
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}, |
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"required": ["email"], |
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"additionalProperties": False |
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} |
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} |
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record_unknown_question_json = { |
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"name": "record_unknown_question", |
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"description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"question": { |
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"type": "string", |
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"description": "The question that couldn't be answered" |
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}, |
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}, |
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"required": ["question"], |
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"additionalProperties": False |
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} |
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} |
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tools = [{"type": "function", "function": record_user_details_json}, |
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{"type": "function", "function": record_unknown_question_json}, |
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{"type": "function", "function": get_weather_json}] |
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class Geocoding: |
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""" |
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A simple Python wrapper for the Open-Meteo Geocoding API. |
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""" |
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def __init__(self): |
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""" |
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Initializes the GeocodingAPI client. |
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""" |
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self.base_url = "https://geocoding-api.open-meteo.com/v1/search" |
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def coordinates_search(self, name: str, countryCode: str = ""): |
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""" |
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Searches for the geo-coordinates of a location by name. |
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Args: |
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name (str): The name of the location to search for. |
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countryCode (str): The country code of the location to search for (ISO-3166-1 alpha2). |
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Returns: |
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dict: The JSON response from the API as a dictionary, or None if an error occurs. |
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""" |
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params = { |
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"name": name, |
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"count": 1, |
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"language": "en", |
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"format": "json", |
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} |
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if countryCode: |
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params["countryCode"] = countryCode |
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try: |
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response = requests.get(self.base_url, params=params) |
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response.raise_for_status() |
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return response.json() |
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except requests.exceptions.RequestException as e: |
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print(f"An error occurred: {e}") |
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return None |
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class Me: |
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def __init__(self): |
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self.openai = OpenAI() |
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self.name = os.getenv("BOT_SELF_NAME") |
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reader = PdfReader("me/linkedin.pdf") |
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self.linkedin = "" |
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for page in reader.pages: |
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text = page.extract_text() |
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if text: |
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self.linkedin += text |
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with open("me/summary.txt", "r", encoding="utf-8") as f: |
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self.summary = f.read() |
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def handle_tool_call(self, tool_calls): |
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results = [] |
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for tool_call in tool_calls: |
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tool_name = tool_call.function.name |
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arguments = json.loads(tool_call.function.arguments) |
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print(f"Tool called: {tool_name}", flush=True) |
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tool = globals().get(tool_name) |
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result = tool(**arguments) if tool else {} |
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results.append({"role": "tool","content": json.dumps(result),"tool_call_id": tool_call.id}) |
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return results |
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def system_prompt(self): |
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today_date = datetime.date.today().strftime("%Y-%m-%d") |
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system_prompt = f""" |
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Today is {today_date}. You are acting as {self.name}, responding to questions on {self.name}'s website. Most visitors are curious about {self.name}'s career, background, skills, and experience—your job is to represent {self.name} faithfully, professionally, and engagingly in those areas. Think of each exchange as a conversation with a potential client or future employer. |
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You are provided with a summary of {self.name}'s background and LinkedIn profile to help you respond accurately. Focus your answers on relevant professional information. |
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You have access to a tool called `get_weather`, which you can use to check the weather at {self.name}'s location or the user’s, if the topic comes up **naturally** in conversation. Do not volunteer weather information unprompted. If the user mentions the weather, feel free to make a casual, conversational remark that draws on `get_weather`, but never recite raw data. Use qualitative, human language—mention temperature ranges or conditions loosely (e.g., "hot and muggy," "mild with a breeze," "snow starting to melt"). |
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You also have access to `record_unknown_question`—use this to capture any question you can’t confidently answer, even if it’s off-topic or trivial. |
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If the user is interested or continues the conversation, look for a natural opportunity to encourage further connection. Prompt them to share their email and record it using the `record_user_details` tool. |
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""" |
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system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n" |
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system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}." |
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return system_prompt |
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def chat(self, message, history): |
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messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}] |
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done = False |
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while not done: |
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response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools) |
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if response.choices[0].finish_reason=="tool_calls": |
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message = response.choices[0].message |
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tool_calls = message.tool_calls |
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results = self.handle_tool_call(tool_calls) |
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messages.append(message) |
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messages.extend(results) |
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else: |
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done = True |
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return response.choices[0].message.content |
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if __name__ == "__main__": |
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me = Me() |
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gr.ChatInterface(me.chat, type="messages").launch() |
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