Update app.py

app.py

@@ -27,14 +27,14 @@ model.to("cpu")
 
 
 def llava(message, history):
-    if message.files:
-        image = message.files[0].path
+    if message["files"]:
+        image = message["files"][0]    
     else:
         for hist in history:
             if type(hist[0])==tuple:
                 image = hist[0][0]
         
-    txt = message.text
+    txt = message["text"]
         
     gr.Info("Analyzing image")
     image = Image.open(image).convert("RGB")
@@ -93,7 +93,7 @@ def respond(message, history):
 
     user_prompt = message
     # Handle image processing
-    if message.files:
+    if message["files"]:
         inputs = llava(message, history)
         streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True})
         generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
@@ -117,7 +117,7 @@ def respond(message, history):
             func_caller.append({"role": "user", "content": f"{str(msg[0])}"})
             func_caller.append({"role": "assistant", "content": f"{str(msg[1])}"})
 
-        message_text = message.text
+        message_text = message["text"]
         func_caller.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant. You have access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }}  </functioncall>  [USER] {message_text}'})
     
         response = client_gemma.chat_completion(func_caller, max_tokens=200)