Update app.py

app.py

@@ -144,23 +144,23 @@ def m4(que, image):
     inputs = processor3(images=image, text=que, return_tensors="pt")
     predictions = model3.generate(**inputs, max_new_tokens=512)
     return processor3.decode(predictions[0], skip_special_tokens=True)
+    
 
 def m5(que, image):
 
-    processor3 = AutoProcessor.from_pretrained("google/pix2struct-ocrvqa-large")
-    model3 = AutoModelForSeq2SeqLM.from_pretrained("google/pix2struct-ocrvqa-large")
+    model3 = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ocrvqa-large")
+    processor3 = Pix2StructProcessor.from_pretrained("google/pix2struct-ocrvqa-large")
 
-    inputs = processor3(images=image, text=que, return_tensors="pt")
 
+    inputs = processor3(images=image, text=que, return_tensors="pt")
     predictions = model3.generate(**inputs)
     return processor3.decode(predictions[0], skip_special_tokens=True)
 
 def m6(que, image):
-    processor3 = AutoProcessor.from_pretrained("google/pix2struct-infographics-vqa-large")
-    model3 = AutoModelForSeq2SeqLM.from_pretrained("google/pix2struct-infographics-vqa-large")
-
+    model3 = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-infographics-vqa-large")
+    processor3 = Pix2StructProcessor.from_pretrained("google/pix2struct-infographics-vqa-large")
+    
     inputs = processor3(images=image, text=que, return_tensors="pt")
-
     predictions = model3.generate(**inputs)
     return processor3.decode(predictions[0], skip_special_tokens=True)