update app.py

app.py

@@ -14,7 +14,7 @@ zero = torch.Tensor([0]).to(device)
 print(f"Device being used: {zero.device}")
 
 @spaces.GPU
-def evaluate_model(model_id):
+def evaluate_model(model_id, num_questions):
     model = SentenceTransformer(model_id, device=device)
     matryoshka_dimensions = [768, 512, 256, 128, 64]
 
@@ -26,7 +26,7 @@ def evaluate_model(model_id):
             "split": "train",
             "size": 7000,
             "columns": ("question", "context"),
-            "sample_size": 500
+            "sample_size": num_questions
         },
         {
             "name": "MLQA",
@@ -35,7 +35,7 @@ def evaluate_model(model_id):
             "split": "validation",
             "size": 500,
             "columns": ("question", "context"),
-            "sample_size": 500
+            "sample_size": num_questions
         },
         {
             "name": "ARCD",
@@ -43,8 +43,8 @@ def evaluate_model(model_id):
             "split": "train",
             "size": None,
             "columns": ("question", "context"),
-            "sample_size": 500,
-            "last_rows": True  # Take the last 500 rows
+            "sample_size": num_questions,
+            "last_rows": True  # Take the last n rows
         }
     ]
     
@@ -58,7 +58,7 @@ def evaluate_model(model_id):
         else:
             dataset = load_dataset(dataset_info["dataset_id"], split=dataset_info["split"])
 
-        # Take last 500 rows if specified
+        # Take the last n rows if specified
         if dataset_info.get("last_rows"):
             dataset = dataset.select(range(len(dataset) - dataset_info["sample_size"], len(dataset)))
         else:
@@ -136,13 +136,17 @@ def evaluate_model(model_id):
     return result_df, charts[0], charts[1], charts[2]
 
 # Define the Gradio interface
-def display_results(model_name):
-    result_df, chart1, chart2, chart3 = evaluate_model(model_name)
+def display_results(model_name, num_questions):
+    result_df, chart1, chart2, chart3 = evaluate_model(model_name, num_questions)
     return result_df, chart1, chart2, chart3
 
+# Gradio interface with a slider to choose the number of questions (1 to 500)
 demo = gr.Interface(
     fn=display_results, 
-    inputs=gr.Textbox(label="Enter a Hugging Face Model ID", placeholder="e.g., Omartificial-Intelligence-Space/GATE-AraBert-v1"), 
+    inputs=[
+        gr.Textbox(label="Enter a Hugging Face Model ID", placeholder="e.g., Omartificial-Intelligence-Space/GATE-AraBert-v1"),
+        gr.Slider(label="Number of Questions", minimum=1, maximum=500, step=1, value=500)
+    ], 
     outputs=[
         gr.Dataframe(label="Evaluation Results"),
         gr.Plot(label="Financial Dataset"),
@@ -164,4 +168,7 @@ demo = gr.Interface(
     css="footer {visibility: hidden;}"
 )
 
-demo.launch(share=True)
+demo.launch(share=True)
+
+# Add the footer
+print("\nCreated by Omar Najar | Omartificial Intelligence Space")