Update app.py

app.py

@@ -1,7 +1,7 @@
-import torch 
+import torch
+import gradio as gr 
 import re 
-import gradio as gr
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
 
 device='cpu'
 encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
@@ -11,7 +11,6 @@ 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)
@@ -20,25 +19,32 @@ def predict(image,max_length=64, num_beams=4):
   caption_text = clean_text(tokenizer.decode(caption_ids))
   return caption_text 
 
-
-
-input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
-output = gr.outputs.Textbox(type="auto",label="Captions")
-examples = [f"example{i}.jpg" for i in range(1,7)]
-
-description= "Image captioning application made using transformers"
-title = "Image Captioning 🖼️"
-
-article = "Created By : Hello Kitty"
-
-interface = gr.Interface(
-        fn=predict,
-        inputs = input,
-        theme="grass",
-        outputs=output,
-        examples = examples,
-        title=title,
-        description=description,
-        article = article,
-    )
-interface.launch(debug=True)
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+css = '''
+h1#title {
+  text-align: center;
+}
+h3#header {
+  text-align: center;
+}
+img#overview {
+  max-width: 800px;
+  max-height: 600px;
+}
+img#style-image {
+  max-width: 1000px;
+  max-height: 600px;
+}
+'''
+demo = gr.Blocks(css=css)
+with demo:
+  gr.Markdown('''<h1 id="title">Image Caption 🖼️</h1>''')
+  gr.Markdown('''Made by : Shreyas Dixit''')
+  with gr.Column():
+        input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
+        output = gr.outputs.Textbox(type="auto",label="Captions")
+  btn = gr.Button("Genrate Caption")
+  btn.click(fn=predict, inputs=input, outputs=output)
+
+demo.launch()