Update app.py

app.py

@@ -10,10 +10,13 @@ from nylon import *
 import pandas as pd
 import json
 import fiber
+import ollama
 print(f"Gradio version: {gr.__version__}")
 
 foldername = 'Celsiaaa'
 dbmsmode = 'Fiber'
+chatmodel = 'llama3.2'
+visionmodel = 'llava'
 
 try:
     with open('settings.arcana',mode='r') as file:
@@ -138,49 +141,67 @@ def execute_function(function_name, function_args):
 
 mapsearchmode = ['always', 'auto', 'none']
 
+def ollama_api_call(messages, vision=False):
+    if vision==True: 
+        response = ollama.chat(
+            model=visionmodel,
+            #messages+=[{
+            #    'images': ['image.jpg']
+            #}]
+        )
+    else:
+        response = ollama.chat(
+            model=chatmodel
+        )
+
+    return messages
+
 def openai_api_call(messages, retries=3, delay=5):
-    global search_mode  # Declare search_mode as a global variable
-
-    for attempt in range(retries):
-        try:
-            # Modify the user's message if search_mode is 0
-            if search_mode == 0:
-                messages[-1]['content'] = "[System: SEARCH when the user ASKED A QUESTION & remember to CITE(the source is the first tag). Otherwise do not search];" + messages[-1]['content']
-
-            completion = client.chat.completions.create(
-                model="gpt-3.5-turbo",
-                messages=messages,
-                functions=function_list,
-                function_call='auto',
-                timeout=10
-            )
-            response_message = completion.choices[0].message
-
-            # Check if the model wants to call a function
-            if response_message.function_call:
-                function_name = response_message.function_call.name
-                function_args = json.loads(response_message.function_call.arguments)
-                function_response = execute_function(function_name, function_args)
-                # Add the function response to the conversation
-                messages.append(response_message.model_dump())  # The model's request to call the function
-                messages.append({
-                    "role": "function",
-                    "name": function_name,
-                    "content": json.dumps(function_response)
-                })
-                # Make a follow-up call to the model with the function response
-                return openai_api_call(messages)
-            else:
-                return response_message.content
-
-        except Exception as e:
-            print(f"Attempt {attempt + 1} failed: {e}")
-            if attempt < retries - 1:
-                time.sleep(delay)
-            else:
-                return "Sorry, I am having trouble connecting to the server. Please try again later."
-
-    return "Failed to get a response after multiple attempts."
+    if online == True:
+        global search_mode  # Declare search_mode as a global variable
+    
+        for attempt in range(retries):
+            try:
+                # Modify the user's message if search_mode is 0
+                if search_mode == 0:
+                    messages[-1]['content'] = "[System: SEARCH when the user ASKED A QUESTION & remember to CITE(the source is the first tag). Otherwise do not search];" + messages[-1]['content']
+    
+                completion = client.chat.completions.create(
+                    model="gpt-3.5-turbo",
+                    messages=messages,
+                    functions=function_list,
+                    function_call='auto',
+                    timeout=10
+                )
+                response_message = completion.choices[0].message
+    
+                # Check if the model wants to call a function
+                if response_message.function_call:
+                    function_name = response_message.function_call.name
+                    function_args = json.loads(response_message.function_call.arguments)
+                    function_response = execute_function(function_name, function_args)
+                    # Add the function response to the conversation
+                    messages.append(response_message.model_dump())  # The model's request to call the function
+                    messages.append({
+                        "role": "function",
+                        "name": function_name,
+                        "content": json.dumps(function_response)
+                    })
+                    # Make a follow-up call to the model with the function response
+                    return openai_api_call(messages)
+                else:
+                    return response_message.content
+    
+            except Exception as e:
+                print(f"Attempt {attempt + 1} failed: {e}")
+                if attempt < retries - 1:
+                    time.sleep(delay)
+                else:
+                    return "Sorry, I am having trouble connecting to the server. Please try again later."
+    
+        return "Failed to get a response after multiple attempts."
+    else:
+         return ollama_api_call()
 
 
 def handle_search_mode(mode):