Update app.py

app.py

@@ -9,26 +9,9 @@ from threading import Thread
 from typing import Generator
 
 
-MODEL_PATH = "fancyfeast/llama-joycaption-alpha-two-vqa-test-1"
-TITLE = "<h1><center>JoyCaption Alpha Two - VQA Test - (2024-11-25a)</center></h1>"
+MODEL_PATH = "fancyfeast/260kxqt2-1199872-llava"
+TITLE = "<h1><center>EXPERIMENTAL MODEL 260kxqt2-1199872</center></h1>"
 DESCRIPTION = """
-<div>
-<p>🧪🧪🧪 This an experiment to see how well JoyCaption Alpha Two can learn to answer questions about images and follow instructions.
-I've only finetuned it on 600 examples, so it is **highly experimental, very weak, broken, and volatile**.  But for only training 600 examples,
-I thought it was performing surprisingly well and wanted to share.</p>
-<p>**This model cannot see any chat history.**</p>
-<p>🧐💬📸 Unlike JoyCaption Alpha Two, you can ask this finetune questions about the image, like "What is he holding in his hand?", "Where might this be?",
-and "What are they wearing?".  It can also follow instructions, like "Write me a poem about this image",
-"Write a caption but don't use any ambigious language, and make sure you mention that the image is from Instagram.", and
-"Output JSON with the following properties: 'skin_tone', 'hair_style', 'hair_length', 'clothing', 'background'." Remember that this was only finetuned on
-600 VQA/instruction examples, so it is _very_ limited right now.  Expect it to frequently fallback to its base behavior of just writing image descriptions.
-Expect accuracy to be lower.  Expect glitches.  Despite that, I've found that it will follow most queries I've tested it with, even outside its training,
-with enough coaxing and re-rolling.</p>
-<p>🚨🚨🚨 If the "Help improve JoyCaption" box is checked, the _text_ query you write will be logged and I _might_ use it to help improve JoyCaption.
-It does not log images, user data, etc; only the text query.  I cannot see what images you send, and frankly, I don't want to.  But knowing what kinds of instructions
-and queries users want JoyCaption to handle will help guide me in building JoyCaption's VQA dataset.  This dataset will be made public.  As always, the model itself is completely
-public and free to use outside of this space.  And, of course, I have no control nor access to what HuggingFace, which are graciously hosting this space, collects.</p>
-</div>
 """
 
 PLACEHOLDER = """
@@ -98,7 +81,7 @@ def chat_joycaption(message: dict, history, temperature: float, top_p: float, ma
 	convo = [
 		{
 			"role": "system",
-			"content": "You are a helpful image captioner.",
+			"content": "You are JoyCaption, a helpful AI assistant with vision capabilities.",
 		},
 		{
 			"role": "user",