Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer
+from llava import LlavaForConditionalGeneration
 
-# Load the quantized model and tokenizer from the Hub
-model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-1.5-7b-hf")
+# Load the LLaVA model and tokenizer from the Hub
+model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
 tokenizer = AutoTokenizer.from_pretrained("llava-hf/llava-1.5-7b-hf")
 
 # Define a function to generate a response given an input text and an optional image URL
 def generate_response(text, image_url=None):
   # Encode the input text and image URL as a single input_ids tensor
-  image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Roadrunner_Petrochelidon_pyrrhonota.jpg/1200px-Roadrunner_Petrochelidon_pyrrhonota.jpg"
   if image_url:
     input_ids = tokenizer(f"{text} <img>{image_url}</img>", return_tensors="pt").input_ids
   else: