Update app.py

app.py

@@ -1,45 +1,45 @@
-# from PIL import Image
-# import requests
-# import gradio as gr
+from PIL import Image
+import requests
+import gradio as gr
 
-# from transformers import BlipProcessor, BlipForConditionalGeneration
+from transformers import BlipProcessor, BlipForConditionalGeneration
 
-# model_id = "Salesforce/blip-image-captioning-base"
+model_id = "Salesforce/blip-image-captioning-base"
 
-# model = BlipForConditionalGeneration.from_pretrained(model_id)
-# processor = BlipProcessor.from_pretrained(model_id)
+model = BlipForConditionalGeneration.from_pretrained(model_id)
+processor = BlipProcessor.from_pretrained(model_id)
 
-# def launch(input):
-#     image = Image.open(requests.get(input, stream=True).raw).convert('RGB')
-#     inputs = processor(image, return_tensors="pt")
-#     out = model.generate(**inputs)
-#     return processor.decode(out[0], skip_special_tokens=True)
+def launch(input):
+    image = Image.open(requests.get(input, stream=True).raw).convert('RGB')
+    inputs = processor(image, return_tensors="pt")
+    out = model.generate(**inputs)
+    return processor.decode(out[0], skip_special_tokens=True)
 
-# iface = gr.Interface(launch, inputs="text", outputs="text")
-# iface.launch()
+iface = gr.Interface(launch, inputs="text", outputs="text")
+iface.launch()
 
-from PIL import Image
-import requests
-from transformers import Blip2Processor, Blip2ForConditionalGeneration
-import torch
+# from PIL import Image
+# import requests
+# from transformers import Blip2Processor, Blip2ForConditionalGeneration
+# import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# device = "cuda" if torch.cuda.is_available() else "cpu"
 
-processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
-model = Blip2ForConditionalGeneration.from_pretrained(
-    "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
-)
-model.to(device)
+# processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+# model = Blip2ForConditionalGeneration.from_pretrained(
+#     "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
+# )
+# model.to(device)
 
-def launch(input):
+# def launch(input):
 
-    image = Image.open(requests.get(input, stream=True).raw)
+#     image = Image.open(requests.get(input, stream=True).raw)
 
-    inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
+#     inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
 
-    generated_ids = model.generate(**inputs)
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-    return generated_text
+#     generated_ids = model.generate(**inputs)
+#     generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+#     return generated_text
 
-iface = gr.Interface(launch, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(launch, inputs="text", outputs="text")
+# iface.launch()