Update app.py

app.py

@@ -1,14 +1,14 @@
-import gradio as gr
+ import gradio as gr
 from PIL import Image
 import io
+from datasets import load_dataset
 import pandas as pd
-from datasets import Dataset
 
-# Create an empty DataFrame
-data = pd.DataFrame(columns=["Image", "Description 1", "Description 2", "Description 3"])
+# Initialize a dataset object for your Hugging Face repository
+dataset = load_dataset("bestroi/Images")
 
-# Function to add a single entry to the dataset
-def add_single_entry(image, desc1, desc2, desc3):
+# Function to handle the user's input and store it in the dataset
+def describe_image(image, desc1, desc2, desc3):
     # Convert the Gradio PIL Image object to bytes
     image_bytes = io.BytesIO()
     
@@ -16,12 +16,20 @@ def add_single_entry(image, desc1, desc2, desc3):
     image.save(image_bytes, format="JPEG")  
     jpeg_data = image_bytes.getvalue()
 
-    data.loc[len(data)] = [jpeg_data, desc1, desc2, desc3]
+    data = {
+        "Image": jpeg_data,
+        "Description 1": desc1,
+        "Description 2": desc2,
+        "Description 3": desc3
+    }
     
-    return "Data entry added successfully"
+    # Add data to the dataset
+    dataset['train'].add_row(**data)
+    
+    return "Data saved successfully"
 
 iface = gr.Interface(
-    fn=add_single_entry,
+    fn=describe_image,
     inputs=[
         gr.Image(type="pil", label="Upload an Image"),
         "text",
@@ -39,8 +47,4 @@ iface = gr.Interface(
     )
 )
 
-iface.launch(share=True)  # This will create a public link when you launch the Gradio interface
-
-# Save the dataset to a file on your local file system
-dataset = Dataset.from_pandas(data)
-dataset.save_to_disk("bestroi/Images")
+iface.launch(share=True)