richtige

app.py

@@ -1,8 +1,8 @@
-
 from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
 import torch
 from PIL import Image
 import gradio as gr
+import numpy as np
 
 model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
@@ -11,30 +11,24 @@ tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning"
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-
-
 max_length = 16
 num_beams = 4
 gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-def predict_step(image_paths):
-  images = []
-  for image_path in image_paths:
-    i_image = Image.open(image_path)
-    if i_image.mode != "RGB":
-      i_image = i_image.convert(mode="RGB")
-
-    images.append(i_image)
 
-  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
-  pixel_values = pixel_values.to(device)
-
-  output_ids = model.generate(pixel_values, **gen_kwargs)
-
-  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-  preds = [pred.strip() for pred in preds]
-  print(preds) 
-  return preds
-
- 
-predict_step(['cat.jpg'])
+def predict_step(image):
+    i_image = Image.fromarray(np.uint8(image))
+    if i_image.mode != "RGB":
+        i_image = i_image.convert(mode="RGB")
+    pixel_values = feature_extractor(images=i_image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+    output_ids = model.generate(pixel_values, **gen_kwargs)
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    return preds
+
+iface = gr.Interface(fn=predict_step, 
+                     inputs=gr.inputs.Image(shape=(224, 224)),
+                     outputs=gr.outputs.Textbox(label="Generated Caption"))
+
+iface.launch(share=True)