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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 | |
| # 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 = "Ed Donner" | |
| # 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. " | |
| # 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 + [{"role": "user", "content": message}] | |
| # done = False | |
| # while not done: | |
| # response = self.openai.chat.completions.create(model="gpt-4o-mini", messages=messages, tools=tools) | |
| # if response.choices[0].finish_reason=="tool_calls": | |
| # message = response.choices[0].message | |
| # tool_calls = message.tool_calls | |
| # results = self.handle_tool_call(tool_calls) | |
| # messages.append(message) | |
| # messages.extend(results) | |
| # else: | |
| # done = True | |
| # return response.choices[0].message.content | |
| # if __name__ == "__main__": | |
| # me = Me() | |
| # gr.ChatInterface(me.chat, type="messages").launch() | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| import json | |
| import os | |
| import requests | |
| from pypdf import PdfReader | |
| import gradio as gr | |
| load_dotenv(override=True) | |
| GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/" | |
| google_api_key = os.getenv("GOOGLE_API_KEY") | |
| # Initialize Gemini client | |
| gemini = OpenAI( | |
| base_url=GEMINI_BASE_URL, | |
| api_key=google_api_key | |
| ) | |
| 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 = gemini # REPLACED OpenAI WITH GEMINI | |
| self.name = "AKASH M J" | |
| reader = PdfReader("me/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, " | |
| f"particularly questions related to {self.name}'s career, background, skills and experience. " | |
| f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. " | |
| f"You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. " | |
| f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. " | |
| f"If you don't know the answer to any question, use your record_unknown_question tool to record the question. " | |
| f"If the user is engaging in discussion, try to steer them towards getting in touch via email." | |
| ) | |
| 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 + [ | |
| {"role": "user", "content": message} | |
| ] | |
| done = False | |
| while not done: | |
| # ---- CHANGED TO USE GEMINI ---- | |
| response = self.openai.chat.completions.create( | |
| model="gemini-2.5-flash", | |
| messages=messages, | |
| tools=tools | |
| ) | |
| # -------------------------------- | |
| if response.choices[0].finish_reason == "tool_calls": | |
| message = response.choices[0].message | |
| tool_calls = message.tool_calls | |
| results = self.handle_tool_call(tool_calls) | |
| messages.append(message) | |
| messages.extend(results) | |
| else: | |
| done = True | |
| return response.choices[0].message.content | |
| if __name__ == "__main__": | |
| me = Me() | |
| # gr.ChatInterface(me.chat, type="messages").launch() | |
| gr.ChatInterface(me.chat).launch() | |