Update app.py

app.py

@@ -5,33 +5,42 @@ from datasets import load_dataset
 import pandas as pd
 
 # Initialize a dataset object for your Hugging Face repository
-dataset = load_dataset("bestroi/Images")
+#dataset = load_dataset("bestroi/Images")
+
+ Load the dataset from Hugging Face
+dataset = hub.load_dataset("bestroi/Images")
+
+# Convert the dataset to a Gradio dataset
+gradio_dataset = gr.Dataset()
+for row in dataset:
+    gradio_dataset.add_row(**row)
 
 # Function to handle the user's input and store it in the dataset
-def describe_image(image, desc1, desc2, desc3):
+def describe_image(image, desc1, desc2, desc3, gradio_dataset):
+
     # Convert the Gradio PIL Image object to bytes
     image_bytes = io.BytesIO()
-    
+
     # Save the image in JPEG format
-    image.save(image_bytes, format="JPEG")  
+    image.save(image_bytes, format="JPEG")
     jpeg_data = image_bytes.getvalue()
 
-    data = {
+    # Add the data to the Gradio dataset
+    gradio_dataset.add_row({
         "Image": jpeg_data,
         "Description 1": desc1,
         "Description 2": desc2,
-        "Description 3": desc3
-    }
-    
-    # Add data to the dataset
-    dataset['train'].add_row(**data)
-    
+        "Description 3": desc3,
+    })
+
     return "Data saved successfully"
 
+# Create the Gradio interface
 iface = gr.Interface(
     fn=describe_image,
     inputs=[
         gr.Image(type="pil", label="Upload an Image"),
+        gr.DatasetInput(type="gradio"),
         "text",
         "text",
         "text"
@@ -47,4 +56,5 @@ iface = gr.Interface(
     )
 )
 
-iface.launch(share=True)  
+# Launch the Gradio interface
+iface.launch(share=True)