Update app.py

app.py

@@ -1,70 +1,141 @@
+import os
+import requests
+from openai import OpenAI
+import json
 import gradio as gr
-from huggingface_hub import InferenceClient
+from pypdf import PdfReader
 
+# ---------------------------
+# Secrets from Hugging Face
+# ---------------------------
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+PUSHOVER_USER = os.getenv("PUSHOVER_USER")
+PUSHOVER_TOKEN = os.getenv("PUSHOVER_TOKEN")
+PUSHOVER_URL = "https://api.pushover.net/1/messages.json"
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+# Initialize OpenAI client
+openai = OpenAI(api_key=OPENAI_API_KEY)
 
-    messages = [{"role": "system", "content": system_message}]
+# ---------------------------
+# Push notification helper
+# ---------------------------
+def push(message):
+    print(f"Push: {message}")
+    payload = {"user": PUSHOVER_USER, "token": PUSHOVER_TOKEN, "message": message}
+    requests.post(PUSHOVER_URL, data=payload)
 
-    messages.extend(history)
+# ---------------------------
+# Tool functions
+# ---------------------------
+def connect(email, name="no name", notes="no notes"):
+    push(f'Someone wants to connect with email: {email}, name: {name}, notes: {notes}')
+    return {"recorded": "ok"}
 
-    messages.append({"role": "user", "content": message})
+def unknown(message):
+    push(f'The question I could not answer was: {message}')
+    return {"recorded": "ok"}
 
-    response = ""
+# JSON schema for tools
+connect_json = {
+    "name": "connect",
+    "description": "Use this tool when someone wants to connect",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "email": {"type": "string", "description": "Email of person wanting to connect"},
+            "name": {"type": "string", "description": "Name of person wanting to connect"},
+            "notes": {"type": "string", "description": "Additional notes"}
+        },
+        "required": ["email"],
+        "additionalProperties": False
+    }
+}
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+unknown_json = {
+    "name": "unknown",
+    "description": "Use this tool when you don't know the answer to a question",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "message": {"type": "string", "description": "The question asked by the user"}
+        },
+        "required": ["message"],
+        "additionalProperties": False
+    }
+}
 
-        response += token
-        yield response
+tools = [{"type": "function", "function": connect_json}, {"type": "function", "function": unknown_json}]
 
+def handle_tool_calls(tool_calls):
+    results = []
+    for tool_call in tool_calls:
+        name = tool_call.function.name
+        print(f'Tool called: {name}')
+        arguments = json.loads(tool_call.function.arguments)
 
+        if name == "unknown":
+            result = unknown(**arguments)
+        elif name == "connect":
+            result = connect(**arguments)
+
+        results.append({"role": "tool", "content": json.dumps(result), "tool_call_id": tool_call.id})
+    return results
+
+# ---------------------------
+# Load PDF & summary
+# ---------------------------
+reader = PdfReader("me/Profile.pdf")
+linkedin = "".join([page.extract_text() or "" for page in reader.pages])
+
+with open("me/summary.txt", "r", encoding="utf-8") as f:
+    summary = f.read()
+
+# ---------------------------
+# System prompt
+# ---------------------------
+name = "Zalaid Butt"
+system_prompt = f"""
+If you don't know the answer, just say you don't know. 
+You are acting as {name}. You are answering questions on {name}'s website, 
+particularly related to {name}'s career, background, skills and experience. 
+Be professional and engaging, as if talking to a potential client or future employer. 
+If you don't know the answer, use the record_unknown_question tool. 
+If the user wants to connect, use the record_user_details tool.
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+system_prompt += f"\n\n## Summary:\n{summary}\n\n## LinkedIn Profile:\n{linkedin}\n\n"
+system_prompt += f"With this context, please chat with the user, always staying in character as {name}. "
+system_prompt += "Always be concise; do not give any additional information if not asked. " \
+                 "If you don't know the answer, use the tool but don't give wrong/fake answers."
+
+# ---------------------------
+# Chat function
+# ---------------------------
+def chat(message, history):
+    messages = [{"role": "system", "content": system_prompt}] + history + [{"role": "user", "content": message}]
+    done = False
+    while not done:
+        response = openai.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            tools=tools
+        )
+
+        finish_reason = response.choices[0].finish_reason
+
+        if finish_reason == "tool_calls":
+            message_obj = response.choices[0].message
+            tool_calls = message_obj.tool_calls
+            result = handle_tool_calls(tool_calls)
+
+            messages.append(message_obj)
+            messages.extend(result)
+        else:
+            done = True
+
+    return response.choices[0].message.content
+
+# ---------------------------
+# Launch Gradio
+# ---------------------------
+gr.ChatInterface(chat, type="messages").launch()