initial

app.py

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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 = "Erik Steinholtz"
+        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__":
+    import gradio as gr
+
+    # Header message
+    me = Me()
+    header = gr.Markdown("# CV chat with Erik Steinholtz")
+
+    # Your picture (local file or URL)
+    # If using a local file, make sure 'me/ESfoto.jpg' is in the same directory as app.py
+    picture = gr.Image("me/ESfoto.jpg", label="About Me", show_label=False, show_download_button=False, elem_id="profile-pic")
+
+    def greet(message, history):
+        # Placeholder function; replace with actual chat logic
+        return "Hello! what do you want to know about me?"
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                header.render()
+                picture.render()
+            with gr.Column(scale=2):
+                gr.ChatInterface(
+                    me.chat,
+                    title="Informational dialogue",
+                    type="messages"
+                    #greet_message="Good day! what do you want to know about me?"
+                )
+                
+
+    demo.launch()

pyproject.toml

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+[project]
+name = "agents"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "anthropic>=0.49.0",
+    "autogen-agentchat>=0.4.9.2",
+    "autogen-ext[grpc,mcp,ollama,openai]>=0.4.9.2",
+    "bs4>=0.0.2",
+    "gradio>=5.22.0",
+    "httpx>=0.28.1",
+    "ipywidgets>=8.1.5",
+    "langchain-anthropic>=0.3.10",
+    "langchain-community>=0.3.20",
+    "langchain-experimental>=0.3.4",
+    "langchain-openai>=0.3.9",
+    "langgraph>=0.3.18",
+    "langgraph-checkpoint-sqlite>=2.0.6",
+    "langsmith>=0.3.18",
+    "lxml>=5.3.1",
+    "mcp-server-fetch>=2025.1.17",
+    "mcp[cli]>=1.5.0",
+    "openai>=1.68.2",
+    "openai-agents>=0.0.15",
+    "playwright>=1.51.0",
+    "plotly>=6.0.1",
+    "polygon-api-client>=1.14.5",
+    "psutil>=7.0.0",
+    "pypdf>=5.4.0",
+    "pypdf2>=3.0.1",
+    "python-dotenv>=1.0.1",
+    "requests>=2.32.3",
+    "semantic-kernel>=1.25.0",
+    "sendgrid>=6.11.0",
+    "setuptools>=78.1.0",
+    "smithery>=0.1.0",
+    "speedtest-cli>=2.1.3",
+    "wikipedia>=1.4.0",
+]
+
+[dependency-groups]
+dev = [
+    "ipykernel>=6.29.5",
+]

requirements.txt

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