running on cpu

app.py

@@ -6,39 +6,39 @@ import spaces
 
 @spaces.GPU
 def infer_infographics(image, question):
-    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
+    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base") 
     processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
 
-    inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
+    inputs = processor(images=image, text=question, return_tensors="pt") 
 
     predictions = model.generate(**inputs)
     return processor.decode(predictions[0], skip_special_tokens=True)
 
 @spaces.GPU
 def infer_ui(image, question):
-    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-screen2words-base").to("cuda")
+    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-screen2words-base") 
     processor = Pix2StructProcessor.from_pretrained("google/pix2struct-screen2words-base")
 
-    inputs = processor(images=image,text=question, return_tensors="pt").to("cuda")
+    inputs = processor(images=image,text=question, return_tensors="pt") 
 
     predictions = model.generate(**inputs)
     return processor.decode(predictions[0], skip_special_tokens=True)
 
 @spaces.GPU
 def infer_chart(image, question):
-  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-chartqa-base").to("cuda")
+  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-chartqa-base") 
   processor = Pix2StructProcessor.from_pretrained("google/pix2struct-chartqa-base")
 
-  inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
+  inputs = processor(images=image, text=question, return_tensors="pt") 
 
   predictions = model.generate(**inputs)
   return processor.decode(predictions[0], skip_special_tokens=True)
 
 @spaces.GPU
 def infer_doc(image, question):
-  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-base").to("cuda")
+  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-base") 
   processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-base")
-  inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
+  inputs = processor(images=image, text=question, return_tensors="pt") 
   predictions = model.generate(**inputs)
   return processor.decode(predictions[0], skip_special_tokens=True)
 

gradio_cached_examples/14/log.csv

@@ -0,0 +1,2 @@
+Answer,flag,username,timestamp
+5,,,2024-04-02 21:26:18.615723

gradio_cached_examples/24/log.csv

@@ -0,0 +1,2 @@
+Answer,flag,username,timestamp
+Vegetarianism,,,2024-04-02 21:27:33.264957

gradio_cached_examples/34/log.csv

@@ -0,0 +1,2 @@
+Caption,flag,username,timestamp
+Your statistics,,,2024-04-02 21:27:38.150716

gradio_cached_examples/44/log.csv

@@ -0,0 +1,2 @@
+Caption,flag,username,timestamp
+40%,,,2024-04-02 21:27:43.219638

requirements.txt

@@ -1,3 +0,0 @@
-transformers
-torch==2.0.1
-spaces