Various improvements

app.py

@@ -20,8 +20,7 @@ except Exception as e:
 
 TOKEN_COSTS = pd.DataFrame.from_dict(TOKEN_COSTS, orient='index').reset_index()
 TOKEN_COSTS.columns = ['model'] + list(TOKEN_COSTS.columns[1:])
-print(TOKEN_COSTS)
-print(TOKEN_COSTS.columns)
+TOKEN_COSTS = TOKEN_COSTS.loc[~TOKEN_COSTS["model"].str.contains("sample_spec")]
 
 
 def count_string_tokens(string: str, model: str) -> int:
@@ -39,11 +38,14 @@ def calculate_total_cost(prompt_tokens: int, completion_tokens: int, model: str)
     return prompt_cost, completion_cost
 
 def update_model_list(function_calling, litellm_provider, max_price):
-    filtered_models = TOKEN_COSTS[
-        (TOKEN_COSTS['litellm_provider'] == litellm_provider)
-    ]
-    list_models = filtered_models['model'].tolist()
-    return gr.Dropdown(choices=list_models, value=list_models[0])
+    if litellm_provider == "Any":
+        return gr.Dropdown(choices=TOKEN_COSTS['model'].tolist(), value=list_models[0]) 
+    else:
+        filtered_models = TOKEN_COSTS[
+            (TOKEN_COSTS['litellm_provider'] == litellm_provider)
+        ]
+        list_models = filtered_models['model'].tolist()
+        return gr.Dropdown(choices=list_models, value=list_models[0])
 
 def compute_all(input_type, prompt_text, completion_text, prompt_tokens, completion_tokens, model):
     if input_type == "Text Input":
@@ -62,7 +64,7 @@ def compute_all(input_type, prompt_text, completion_text, prompt_tokens, complet
         f"${total_cost:.6f}"
     )
 
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+with gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.yellow, secondary_hue=gr.themes.colors.orange)) as demo:
     gr.Markdown("""
     # Text-to-$$$: Calculate the price of your LLM runs
     Based on data from [litellm](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
@@ -80,17 +82,17 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             with gr.Group(visible=False) as token_input_group:
                 prompt_tokens_input = gr.Number(label="Prompt Tokens (thousands)", value=1.5)
                 completion_tokens_input = gr.Number(label="Completion Tokens (thousands)", value=2)
-            
+
             gr.Markdown("## Model choice:")
             with gr.Row():
                 function_calling = gr.Checkbox(label="Supports Function Calling", value=False)
-                litellm_provider = gr.Dropdown(label="LiteLLM Provider", choices=TOKEN_COSTS['litellm_provider'].unique().tolist())
+                litellm_provider = gr.Dropdown(label="LiteLLM Provider", choices=["Any"] + TOKEN_COSTS['litellm_provider'].unique().tolist(), value="Any")
             
             max_price = gr.Slider(label="Max Price per Token (input + output)", minimum=0, maximum=0.001, step=0.00001, value=0.001)
             
-            model = gr.Dropdown(label="Model", choices=TOKEN_COSTS['model'].tolist())
+            model = gr.Dropdown(label="Model", choices=TOKEN_COSTS['model'].tolist(), value=TOKEN_COSTS['model'].tolist()[0])
             
-            compute_button = gr.Button("Compute Costs", variant="primary")
+            compute_button = gr.Button("Compute Costs", variant="secondary")
 
         with gr.Column(scale=1):
             with gr.Group():