Update app.py

app.py

@@ -11,23 +11,32 @@ from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecode
 # iface.launch()
 
 device='cpu'
-encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
+encoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
+decoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
+model_checkpoint = "ydshieh/vit-gpt2-coco-eng"
 feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
 tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
 model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
 
 
 def predict(image,max_length=64, num_beams=4):
-  image = image.convert('RGB')
-  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  caption_ids = model.generate(image, max_length = max_length)[0]
-  caption_text = clean_text(tokenizer.decode(caption_ids))
-  return caption_text 
-
-st.title("Image to Text using Lora")
+    input_image = Image.open(image)
+    model.eval()
+    pixel_values = feature_extractor(images=[input_image], return_tensors="pt").pixel_values
+    with torch.no_grad():
+        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]  
+    return preds[0]
+    
+  # image = image.convert('RGB')
+  # image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
+  # clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
+  # caption_ids = model.generate(image, max_length = max_length)[0]
+  # caption_text = clean_text(tokenizer.decode(caption_ids))
+  # return caption_text 
+
+# st.title("Image to Text using Lora")
 
 inputs = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
 output = gr.outputs.Textbox(type="text",label="Captions")