local runtime ok

app.py

@@ -1,14 +1,13 @@
-import torch 
-import re 
+import torch
 import gradio as gr
-from PIL import Image
 
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
 import os
-import tensorflow as tf
+
 os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
-device='cpu'
+device = 'cpu'
+
 
 model_id = "nttdataspain/vit-gpt2-stablediffusion2-lora"
 model = VisionEncoderDecoderModel.from_pretrained(model_id)
@@ -27,18 +26,15 @@ def predict(image):
     preds = [pred.strip() for pred in preds]
     return preds[0]
 
-input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
-output = gr.outputs.Textbox(type="text",label="Captions")
 examples_folder = os.path.join(os.path.dirname(__file__), "examples")
 examples = [os.path.join(examples_folder, file) for file in os.listdir(examples_folder)]
 
 with gr.Blocks() as demo:
-    
     gr.HTML(
         """
         <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
         <h2 style="font-weight: 900; font-size: 3rem; margin: 0rem">
-            📸 ViT Image-to-Text with LORA 📝
+            📸 Video Image Info with LORA 📝
         </h2>   
         <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 2rem; margin-bottom: 1.5rem">
         In the field of large language models, the challenge of fine-tuning has long perplexed researchers. Microsoft, however, has unveiled an innovative solution called <b>Low-Rank Adaptation (LoRA)</b>. With the emergence of behemoth models like GPT-3 boasting billions of parameters, the cost of fine-tuning them for specific tasks or domains has become exorbitant.
@@ -48,21 +44,16 @@ with gr.Blocks() as demo:
         </h2>
         </div>
         """)
-    
-    with gr.Row():
-            with gr.Column(scale=1):
-                img = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
-                button = gr.Button(value="Describe")
-            with gr.Column(scale=1):
-                out = gr.outputs.Textbox(type="text",label="Captions")   
-                
+
+    img = gr.Image(label="Upload any Image", type='pil')
+    button = gr.Button(value="Describe")
+    out = gr.Textbox(type="text", label="Captions")
+
     button.click(predict, inputs=[img], outputs=[out])
- 
-    gr.Examples(
-        examples=examples,
+
+    gr.Interface(
         inputs=img,
         outputs=out,
         fn=predict,
-        cache_examples=True,
     )
-demo.launch(debug=True)
+demo.launch(debug=True)

requirements.txt

@@ -4,4 +4,4 @@ pillow
 requests
 torch
 tensorflow
-gradio == 3.50
+gradio == 4.29