Update app.py

app.py

@@ -7,31 +7,43 @@ dataset = load_dataset("huggan/wikiart", streaming=True)
 
 # Function to display artwork details
 def display_artwork(index):
-    for i, record in enumerate(dataset["train"]):
-        if i == index:
-            return record["image"], f"Welcome to the Gallery\n\nTitle: {record['title']}\nArtist: {record['artist']}\nStyle: {record['style']}\nGenre: {record['genre']}\n\nStep into the world of art and explore its details."
+    try:
+        for i, record in enumerate(dataset["train"]):  # Stream through the dataset
+            if i == index:
+                return record["image"], f"Title: {record['title']}\nArtist: {record['artist']}\nStyle: {record['style']}\nGenre: {record['genre']}"
+        return None, "Error: Index out of range or invalid."
+    except Exception as e:
+        return None, f"Error: {str(e)}"
 
 # Function to filter artworks based on metadata
 def filter_artworks(artist=None, genre=None, style=None):
     results = []
-    for record in dataset["train"]:
-        if (artist is None or record["artist"] == artist) and \
-           (genre is None or record["genre"] == genre) and \
-           (style is None or record["style"] == style):
-            results.append(record)
+    try:
+        for record in dataset["train"]:
+            if (artist is None or record["artist"] == artist) and \
+               (genre is None or record["genre"] == genre) and \
+               (style is None or record["style"] == style):
+                results.append(record)
+    except Exception as e:
+        return []
     return results
 
 # Function to display filtered artworks
 def display_filtered_artworks(artist, genre, style):
     filtered_results = filter_artworks(artist, genre, style)
+    if len(filtered_results) == 0:
+        return None, "No artworks found with the specified filters."
     return [(r["image"], f"Title: {r['title']}\nArtist: {r['artist']}\nStyle: {r['style']}\nGenre: {r['genre']}") for r in filtered_results]
 
 # Chatbot functionality for museum guide persona using a publicly available Hugging Face model
 chatbot = pipeline("text-generation", model="gpt2")  # Replace with a valid Hugging Face model
 
 def museum_guide_query(prompt):
-    response = chatbot(prompt, max_length=100, num_return_sequences=1)
-    return response[0]["generated_text"]
+    try:
+        response = chatbot(prompt, max_length=100, num_return_sequences=1)
+        return response[0]["generated_text"]
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Gradio interfaces
 artwork_interface = gr.Interface(