Adding in tool calling

app.py

@@ -6,10 +6,10 @@ from openai import OpenAI
 # from IPython.display import display, Markdown
 import gradio as gr 
 # from dotenv import load_dotenv
-# import json
+import json
 import uuid
-# import random
-# import requests
+import random
+import requests
 import chromadb
 from pprint import pprint
 
@@ -346,6 +346,140 @@ collection.add(
     documents=chunks
 )
 
+#Tools-----------------------------
+tools = []
+
+#Tool Handling --------------------
+#PUSHOVER CREDENTIALS
+#Creating the pushover identification information
+pushover_user = os.getenv("PUSHOVER_USER")
+pushover_token = os.getenv("PUSHOVER_TOKEN")
+pushover_url = 'https://api.pushover.net/1/messages.json'
+
+def send_notifications(message:str):
+    payload = {'user' : pushover_user,"token" : pushover_token, 'message': message}
+    requests.post(url = pushover_url, data=payload)
+
+#PUSHOVER CREDENTIALS
+#Creating the pushover identification information
+pushover_user = os.getenv("PUSHOVER_USER")
+pushover_token = os.getenv("PUSHOVER_TOKEN")
+pushover_url = 'https://api.pushover.net/1/messages.json'
+
+# print(pushover_user)
+
+#creating the def send notifications:
+# import requests
+
+def send_notifications(message:str):
+    payload = {'user' : pushover_user,"token" : pushover_token, 'message': message}
+    requests.post(url = pushover_url, data=payload)
+
+#Testing send notification
+# send_notifications('June 8, 2026 Three Whistles')
+
+# This is not a dictionary. This is a description card written in JSON format.
+# We are describing the tool to OpenAI so it knows how to use it.
+# Think of it like a form or a menu card. Not real Python objects.
+#DESCRIBE PUSHOVER AS AN LLM TOOL
+send_notifications_function = {
+
+    # The name of the tool OpenAI will request when it wants to use it
+    'name': 'send_notifications',
+
+    # When should OpenAI use this tool? This description tells it.
+    'description': 'Sends a push notification to the real-world version of you via Pushover on mobile. Use this if the user needs to alert the real-world version of you',
+    'parameters': {
+
+        'type': 'object',
+        'properties': {
+
+            'message': {
+                'type': 'string',
+
+                'description': 'The notification message the user wants sent to their device'
+            }
+
+            
+        },
+
+        
+        'required': ['message']
+    }
+}
+
+#adding this tool to the tool list:
+tools.append({"type" : "function", "function":send_notifications_function})
+
+#Dice_roll---------------
+# import random
+
+#creating roll dice function
+def dice_roll():
+    result = random.randint(1,6)
+    return result
+
+#creating roll dice description of tool for LLM:
+roll_dice_function = {
+    'name' : "dice_roll",
+    'description': 'This is a function where you can roll a dice and can see the number you get from the roll',
+    "parameters": {
+
+    },
+    "required" : []
+}
+
+#appending this to our available tools
+tools.append({"type" : "function", "function" : roll_dice_function})
+
+
+#Handle Tool Call -----------------
+def handle_tool_call(tool_calls:list):
+        
+
+        tool_call_results = []
+
+        for tool_call in tool_calls:
+
+            function_name = tool_call.function.name
+
+            #  --- for debugging
+            # print(f"the function name is {function_name}")
+                
+            # tool_call = tools_calls[0] #this is because we only have one tool thus far
+            
+            args = json.loads(tool_call.function.arguments)
+
+            if function_name == 'send_notifications':
+                send_notifications(args['message']) #sent to pushover
+
+                content = f"Sent Notification: {args['message']}"
+
+            elif function_name == "dice_roll":
+
+                content =  f' Rolled: {dice_roll()}'
+
+            # elif function_name == "insert_function3_name":
+
+            #     content =insert_function3_name (args['message']})
+
+            else:
+                 content = f"Unknown function: {function_name}"
+
+            #package into a dictionary for the llm to see what we got out from the tool call
+            tool_call_result = {
+            'role' : 'tool',
+            'content' : content ,
+                'tool_call_id' : tool_call.id   
+            }
+
+            print(f"this is what the tool_call_result from handle tool_call looks like: {tool_call_result}")
+
+            #appending each tool_call_result to the tool_call_results list of dictionaries
+            tool_call_results.append(tool_call_result)
+
+        return tool_call_results
+
 
 #System message---------------------
 system_message = """
@@ -384,7 +518,7 @@ def respond_system_enhanced (message, history):
     )
     
     #logs for debugging
-    #lets just give the closest distance one as context
+    #lets just give the 3 most relevant chunks
     context = "\n--\n".join(results['documents'][0])
     print(f'User message:\n{message}\n')
     print('Context this turn:\n', context)
@@ -401,12 +535,37 @@ def respond_system_enhanced (message, history):
     # Call LLM
     response = client.chat.completions.create(
         model = "gpt-4.1-mini",
-        messages = messages
+        messages = messages,
+        tools = tools
     )
 
-    
+    #record message 
     message = response.choices[0].message
 
+    print("message before while loop", message)
+
+
+    
+    while message.tool_calls:
+        # from pprint import pprint
+        pprint(message.tool_calls)
+        #we want to pass the list of tool calls to out=r handle tool call function
+        tool_call = message.tool_calls
+        tool_call_result = handle_tool_call(message.tool_calls)
+
+        #we want to append the message with the user request and the first two tool call requests
+        messages.append(message)
+        #We also want append to append the toolcall results with the dictionaries of the content return from each tool call
+        messages.extend(tool_call_result)
+
+        response = client.chat.completions.create(
+            model = "gpt-4.1-mini",
+            messages = messages,
+            tools=tools
+        )
+
+        message = response.choices[0].message
+
     return (message.content)
 
 

requirements.txt

@@ -7,4 +7,6 @@
 #to install into our .py file
 gradio
 openai
-chromadb
+chromadb
+requests
+random