Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import pandas as pd
+from datasets import load_dataset 
+import transformers
+from transformers import AutoProcessor
+from transformers import AutoModelForCausalLM
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torchvision.transforms import Resize
+import os
+from PIL import Image
+saved_folder_path = "sudeep-007/saved_model"
+processor = AutoProcessor.from_pretrained(saved_folder_path)
+model = AutoModelForCausalLM.from_pretrained(saved_folder_path)
+def generate_caption(image):
+    # Process the image
+    image = Image.fromarray(image)
+    #inputs = tokenizer(image, return_tensors="pt")
+    inputs = processor(images=image, return_tensors="pt")#.to(device)
+    pixel_values = inputs.pixel_values
+
+    # Generate caption
+    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    return generated_caption
+interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(),
+    outputs=gr.Textbox(),
+    live=True
+)
+interface.queue()
+interface.launch(share=True)