Upload folder using huggingface_hub

.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gitignore

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+# Environment variables (API keys, secrets)
+.env
+.env.local
+.env.*.local
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+.venv/
+env/
+*.egg-info/
+.eggs/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db

README.md

@@ -1,12 +1,6 @@
 ---
-title: CV AI CHAT
-emoji: 🐢
-colorFrom: red
-colorTo: purple
+title: CV-AI-CHAT
+app_file: app.py
 sdk: gradio
 sdk_version: 6.8.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# --- Dependencies ---
+from dotenv import load_dotenv
+from openai import OpenAI
+import json
+import os
+import requests
+from pypdf import PdfReader
+import gradio as gr
+
+# Load environment variables from .env file (e.g. OPENAI_API_KEY, PUSHOVER_TOKEN)
+load_dotenv(override=True)
+
+
+# --- Pushover Integration (mobile notifications) ---
+def push(text):
+    """Send a notification to your phone via the Pushover API."""
+    requests.post(
+        "https://api.pushover.net/1/messages.json",
+        data={
+            "token": os.getenv("PUSHOVER_TOKEN"),
+            "user": os.getenv("PUSHOVER_USER"),
+            "message": text,
+        }
+    )
+
+
+# --- Tool functions (callable by the AI when it decides to) ---
+def record_user_details(email, name="Name not provided", notes="not provided"):
+    """When a user wants to get in touch: send their contact info to your phone and acknowledge."""
+    push(f"Recording {name} with email {email} and notes {notes}")
+    return {"recorded": "ok"}
+
+
+def record_unknown_question(question):
+    """When the AI doesn't know the answer: log the question so you can follow up later."""
+    push(f"Recording {question}")
+    return {"recorded": "ok"}
+
+
+# --- Tool schemas (OpenAI function calling format) ---
+# These JSON objects describe each tool so the model knows when and how to call them.
+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
+    }
+}
+
+# List of tools exposed to the AI model (OpenAI function-calling format)
+tools = [{"type": "function", "function": record_user_details_json},
+        {"type": "function", "function": record_unknown_question_json}]
+
+
+# --- Main agent: persona chatbot ---
+class Me:
+
+    def __init__(self):
+        """Initialize the agent: connect to OpenAI and load persona knowledge from files."""
+        self.openai = OpenAI()
+        self.name = "Harold Malécot"
+        # Load LinkedIn profile text from PDF (one string per page concatenated)
+        reader = PdfReader("linkedin.pdf")
+        self.linkedin = ""
+        for page in reader.pages:
+            text = page.extract_text()
+            if text:
+                self.linkedin += text
+        # Use professional email instead of personal one when displayed/shared
+        self.linkedin = self.linkedin.replace("harold.malecot@proton.me", "harold.job@proton.me")
+        # Load additional summary text (e.g. bio, key points)
+        with open("summary.txt", "r", encoding="utf-8") as f:
+            self.summary = f.read()
+
+
+    def handle_tool_call(self, tool_calls):
+        """Run each tool the model requested and return formatted responses for the next API call."""
+        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)
+            # Resolve the actual Python function by name and call it
+            tool = globals().get(tool_name)
+            result = tool(**arguments) if tool else {}
+            # OpenAI expects tool results in this format to continue the conversation
+            results.append({"role": "tool", "content": json.dumps(result), "tool_call_id": tool_call.id})
+        return results
+    
+    def system_prompt(self):
+        """Build the system prompt that defines the AI's persona and behavior."""
+        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 sharing your contact email, always use harold.job@proton.me (never use any other email address). "
+        # Append the knowledge base (summary + LinkedIn text) so the model can answer accurately
+        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):
+        """Gradio callback: build messages, call OpenAI, handle tool calls in a loop, return final text."""
+        # Assemble full conversation: system prompt + prior turns + latest user message
+        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":
+                # Model wants to call tools: run them and add results to the conversation
+                message = response.choices[0].message
+                tool_calls = message.tool_calls
+                results = self.handle_tool_call(tool_calls)
+                messages.append(message)
+                messages.extend(results)
+                # Loop again so the model can use tool results and produce a final reply
+            else:
+                # Model finished with text; we're done
+                done = True
+        return response.choices[0].message.content
+
+
+# --- Entry point: launch the Gradio chat UI ---
+if __name__ == "__main__":
+    me = Me()
+    # Gradio ChatInterface: fn gets (message, history) with history as OpenAI-style message dicts (Gradio 6 default)
+    gr.ChatInterface(me.chat).launch()
+    

requirements.txt

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+requests
+python-dotenv
+gradio
+pypdf
+openai
+openai-agents

summary.txt

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+My name is Harold Malécot. I'm an entrepreneur and software engineer. I live in south of France.
+I love cooking, traveling, entrepreneurship, IT development, and I am passionate about AI and blockchain. I also love the NBA, UFC, and sports in general.