Update app.py

app.py

@@ -1,18 +1,20 @@
-from transformers import AutoTokenizer, AutoModelForVision2Seq
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
 import torch
-
 # Define the model path
 MODEL_NAME = "RPW/NIH-1.2_Llama-3.2-11B-Vision-Instruct"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForVision2Seq.from_pretrained(MODEL_NAME).to(device)
+tokenizer = RobertaTokenizer.from_pretrained(MODEL_NAME)
+model = RobertaForSequenceClassification.from_pretrained(MODEL_NAME).to(device)
 
 # Example usage: Tokenizing some input text
 input_text = "Describe this image."
 inputs = tokenizer(input_text, return_tensors="pt").to(device)
 
 # Generating output
-output = model.generate(**inputs)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
+outputs = model(**inputs)
+logits = outputs.logits
+
+# ใช้ softmax หรือการแปลงค่าอื่นๆ ถ้าต้องการผลลัพธ์ที่เหมาะสมกับการ classification
+print(logits)