Parameters slider (#5)

- Added params slider (c21d6446662501fd5016a6dc5ff399ab5b561fad)

app.py

@@ -12,6 +12,20 @@ ds_results = load_dataset("visionLMsftw/vibe-testing-results", split="train")
 models = get_model_names(ds_results)
 responses = get_responses(ds_results)
 
+model_params = {
+    "Qwen/Qwen2.5-VL-32B-Instruct": 32,
+    "google/gemma-3-27b-it": 27,
+    "meta-llama/Llama-4-Maverick-17B-128E-Instruct": 17,
+    "HuggingFaceTB/SmolVLM2-2.2B-Instruct": 2.2,
+}
+
+def filter_models_by_param(min_params):
+    filtered_models = [m for m, p in model_params.items() if p >= min_params]
+    selected = filtered_models[0] if filtered_models else None
+    return gr.update(choices=filtered_models, value=selected)
+
+models = list(model_params.keys())
+
 default_category = evaluation_data[0]["category"]
 default_example_id = evaluation_data[0]["id"]
 
@@ -22,7 +36,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     mode = gr.Radio(["View model-wise responses", "Compare model responses on a specific example"], label="Mode", value="View model-wise responses")
 
     with gr.Column(visible=True) as model_mode:
+        param_slider = gr.Slider(minimum=2, maximum=32, step=1, label="Minimum model parameters (B)")
         selected_model = gr.Dropdown(models, label="Choose model")
+
+        param_slider.change(filter_models_by_param, inputs=param_slider, outputs=selected_model)
         model_category = gr.Dropdown(
             choices=list(set(ex["category"] for ex in evaluation_data)),
             label="Category",