Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+
+# Define the LLM models and their properties
+models = {
+    "gpt-4o-2024-08-06": {
+        "input_price_per_1M": 2.50,
+        "output_price_per_1M": 10.00,
+        "max_input_tokens": 128_000,
+    },
+    "gpt-4o-mini-2024-07-18": {
+        "input_price_per_1M": 0.15,
+        "output_price_per_1M": 0.600,
+        "max_input_tokens": 128_000,
+    },
+    "Claude 3.5 Sonnet": {
+        "input_price_per_1M": 3.0,
+        "output_price_per_1M": 15.0,
+        "max_input_tokens": 200_000,
+    },
+    "GPT-3.5-turbo": {
+        "input_price_per_1M": 0.5,
+        "output_price_per_1M": 1.5,
+        "max_input_tokens": 4096,
+    },
+    "GPT-4": {
+        "input_price_per_1M": 30.0,
+        "output_price_per_1M": 60.0,
+        "max_input_tokens": 8192,
+    },
+}
+
+def calculate_cost(model, input_tokens, output_tokens, num_requests):
+    if model not in models:
+        return "Invalid model selected", 0, 0, 0
+
+    if input_tokens > models[model]["max_input_tokens"]:
+        return f"Input tokens exceed the maximum limit for {model}", 0, 0, 0
+
+    input_cost = (input_tokens / 1_000_000) * models[model]["input_price_per_1M"] * num_requests
+    output_cost = (output_tokens / 1_000_000) * models[model]["output_price_per_1M"] * num_requests
+    total_cost = input_cost + output_cost
+
+    return f"${total_cost:.6f}", input_cost, output_cost, total_cost
+
+def compare_models(input_tokens, output_tokens, num_requests):
+    results = []
+    for model in models:
+        total_cost_str, input_cost, output_cost, total_cost = calculate_cost(
+            model, input_tokens, output_tokens, num_requests
+        )
+        results.append(
+            {
+                "Model": model,
+                "Input Cost": f"${input_cost:.6f}",
+                "Output Cost": f"${output_cost:.6f}",
+                "Total Cost": total_cost_str,
+                "Max Input Tokens": models[model]["max_input_tokens"],
+                "Input Price (1M)": f"${models[model]['input_price_per_1M']:.2f}",
+                "Output Price (1M)": f"${models[model]['output_price_per_1M']:.2f}",
+            }
+        )
+    return pd.DataFrame(results)
+
+def create_interface():
+    with gr.Blocks() as interface:
+        gr.Markdown("# LLM Price Comparison Tool")
+        with gr.Row():
+            input_tokens = gr.Number(label="Input Tokens", value=100)
+            output_tokens = gr.Number(label="Output Tokens", value=100)
+            num_requests = gr.Number(label="Number of Requests", value=1, step=1)
+        compare_btn = gr.Button("Compare Models")
+        output_table = gr.DataFrame(label="Comparison Results")
+
+        compare_btn.click(
+            fn=compare_models,
+            inputs=[input_tokens, output_tokens, num_requests],
+            outputs=output_table,
+        )
+
+    return interface
+
+# Create and launch the interface
+demo = create_interface()
+
+# Hugging Face specific launch
+if __name__ == "__main__":
+    demo.launch()
+else:
+    demo.launch(share=True)