plot to info c & v

app.py

@@ -163,14 +163,19 @@ def m5(que, image):
     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")
+    # processor3 = AutoProcessor.from_pretrained("google/pix2struct-infographics-vqa-large")
+    # model3 = AutoModelForSeq2SeqLM.from_pretrained("google/pix2struct-infographics-vqa-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)
+    # predictions = model3.generate(**inputs)
+    # return processor3.decode(predictions[0], skip_special_tokens=True)
+    processor3 = Pix2StructProcessor.from_pretrained('google/matcha-plotqa-v2')
+    model3 = Pix2StructForConditionalGeneration.from_pretrained('google/matcha-plotqa-v2')
 
+    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 predict_answer(category, que, image):
     print(f"category is THIS {category}")