Add PDF with LFS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+me/*.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,4 +1,5 @@
 ---
+<<<<<<< HEAD
 title: Career Conversation
 emoji: 🏃
 colorFrom: red
@@ -10,3 +11,10 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+=======
+title: career_alter_ego
+app_file: app.py
+sdk: gradio
+sdk_version: 5.49.1
+---
+>>>>>>> d9aab13 (Initial commit)

app.py

@@ -0,0 +1,134 @@
+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 = "Ukweh Chima Everest"
+        reader = PdfReader("me/cv.pdf")
+        self.linkedin = "https://www.linkendin.com/in/ukweheverest"
+        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()
+    

me/cv.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bfc1df2565f1fc30819f4f1e8aa07b1ac79516877b42a7f62d2d21f6ee06193
+size 283953

me/summary.txt

@@ -0,0 +1,13 @@
+Everest China Ukweh is a highly experienced Full-Stack Software Engineer with over 7 years of expertise in designing and scaling platforms across fintech, edtech, SaaS, and AI. He specializes in backend architecture, API development, cloud infrastructure, and AI integration, with a proven track record of delivering systems used by over 20,000 users.
+
+His technical proficiency spans Python, Django, Node.js, NestJS, Laravel, Java, and modern JavaScript/TypeScript stacks. He has extensive experience in full-cycle product development—from system design to deployment and optimization.
+
+Recent work includes fine-tuning large language models (LLMs) and implementing Retrieval-Augmented Generation (RAG) systems at Revial.tech, as well as backend development using NestJS at Urban.ng. He has also held senior roles at Management System Global, Orion Industrial Resources, Walkre.com, and Amabilis Technologies, where he led full-stack development, API design, database engineering, and team collaboration.
+
+In addition to development, Everest has experience as a programming and cybersecurity instructor, where he trained students in Python, PHP, Django, Laravel, and networking. His background includes client-facing roles in customer service and wallet management, demonstrating strong communication and operational skills.
+
+He holds a Bachelor of Technology in Environmental Engineering from the Federal University of Technology Owerri and has earned numerous certifications in software development, cybersecurity, AI, and machine learning from platforms like HackerRank and Zero to Mastery.
+
+His project portfolio includes high-traffic platforms such as urban.ng, walkre.com, and 360corporation.co, as well as machine learning projects in sales forecasting and AI-generated content detection.
+
+Everest is proficient in English, Igbo, and Hausa, and is based in Abuja, Nigeria.

requirements.txt

@@ -0,0 +1,6 @@
+requests
+python-dotenv
+gradio
+pypdf
+openai
+openai-agents