Update app.py

app.py

@@ -6,11 +6,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 #Setting device to cuda
 torch.set_default_device("cuda")
 
-torch.hub.download_url_to_file('https://github.com/manishkumart/SparrowVQE/blob/main/data/Images/week_01/week_01_page_024.png', 'week_01_page_024.png')
-
-# https://github.com/manishkumart/SparrowVQE/blob/main/data/Images/week_01/week_01_page_024.png
-
-
 # # Ensure GPU usage if available
 # device = "cuda" if torch.cuda.is_available() else "cpu"
 # torch.set_default_tensor_type('torch.cuda.FloatTensor' if device=='cuda' else 'torch.FloatTensor')
@@ -46,8 +41,16 @@ def predict_answer(image, question, max_tokens):
 def gradio_predict(image, question, max_tokens):
     answer = predict_answer(image, question, max_tokens)
     return answer
+    
+examples = [["data/week_01_page_024.png", 'Can you explain the slide?'], 
+            ["data/week_03_page_091.png", 'Can you explain the slide?'], 
+            ["data/week_01_page_062.png", 'Are the training images labeled?'], 
+            ["data/week_05_page_027.png", 'What is meant by eigenvalue multiplicity?']],
+            ["data/week_05_page_030.png", 'What does K represent?'], 
+            ["data/week_15_page_046.png", 'How are individual heterogeneous models trained?'], 
+            ["data/week_15_page_021.png", 'How does Bagging affect error?']],
+            ["data/week_15_page_037.png", 'What does the '+' and '-' represent?']]
 
-examples = [["week_01_page_024.png", "Can you explain the slide?"]]
 # Define the Gradio interface
 iface = gr.Interface(
     fn=gradio_predict,