Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,7 +1,5 @@
 import streamlit as st
 import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
 
 st.set_page_config(page_title="LLM API Budget Dashboard", layout="wide")
 
@@ -11,10 +9,10 @@ st.markdown("This dashboard helps you budget your API calls to various LLMs base
 
 # Define LLM models and their costs
 llm_data = {
-    "GPT-4o": {"input_cost_per_m": 2.50, "output_cost_per_m": 10.00},
-    "Claude 3.7 Sonnet": {"input_cost_per_m": 3.00, "output_cost_per_m": 15.00},
-    "Gemini Flash 1.5-8b": {"input_cost_per_m": 0.038, "output_cost_per_m": 0.15},
-    "o3-mini": {"input_cost_per_m": 1.10, "output_cost_per_m": 4.40}
+    "GPT-4o": {"input_cost_per_m": 2.50, "output_cost_per_m": 10.00, "description": "OpenAI's GPT-4o model"},
+    "Claude 3.7 Sonnet": {"input_cost_per_m": 3.00, "output_cost_per_m": 15.00, "description": "Anthropic's Claude 3.7 Sonnet model"},
+    "Gemini Flash 1.5-8b": {"input_cost_per_m": 0.038, "output_cost_per_m": 0.15, "description": "Google's Gemini Flash 1.5-8b model"},
+    "o3-mini": {"input_cost_per_m": 1.10, "output_cost_per_m": 4.40, "description": "Created Jan 31, 2025, 200k context"}
 }
 
 # Convert the LLM data to a DataFrame for displaying in a table
@@ -22,7 +20,8 @@ llm_df = pd.DataFrame([
     {
         "Model": model, 
         "Input Cost ($/M tokens)": data["input_cost_per_m"], 
-        "Output Cost ($/M tokens)": data["output_cost_per_m"]
+        "Output Cost ($/M tokens)": data["output_cost_per_m"],
+        "Description": data["description"]
     } 
     for model, data in llm_data.items()
 ])
@@ -31,131 +30,107 @@ llm_df = pd.DataFrame([
 st.subheader("LLM Cost Information")
 st.dataframe(llm_df, use_container_width=True)
 
-# Create sidebar for inputs
-st.sidebar.header("Configuration")
+# Create columns for main layout
+left_col, right_col = st.columns([1, 3])
 
-# Token input section
-st.sidebar.subheader("Token Settings")
-input_tokens = st.sidebar.number_input("Input Tokens", min_value=1, value=1000, step=100)
-output_tokens = st.sidebar.number_input("Output Tokens", min_value=1, value=500, step=100)
-
-# LLM selection
-st.sidebar.subheader("Select LLMs")
-selected_llms = st.sidebar.multiselect("Choose LLMs", options=list(llm_data.keys()), default=list(llm_data.keys()))
-
-# Run count settings
-st.sidebar.subheader("Run Count Settings")
-uniform_runs = st.sidebar.checkbox("Run all LLMs the same number of times", value=True)
-
-if uniform_runs:
-    uniform_run_count = st.sidebar.number_input("Number of runs for all LLMs", min_value=1, value=1, step=1)
-    run_counts = {llm: uniform_run_count for llm in selected_llms}
-else:
-    st.sidebar.write("Set individual run counts for each LLM:")
-    run_counts = {}
-    for llm in selected_llms:
-        run_counts[llm] = st.sidebar.number_input(f"Runs for {llm}", min_value=1, value=1, step=1)
-
-# Stability test settings
-st.sidebar.subheader("Stability Test Settings")
-stability_test = st.sidebar.checkbox("Enable stability testing", value=False)
-
-if stability_test:
-    st.sidebar.write("Set stability iterations for selected LLMs:")
-    stability_iterations = {}
-    for llm in selected_llms:
-        stability_enabled = st.sidebar.checkbox(f"Test stability for {llm}", value=False)
-        if stability_enabled:
-            iterations = st.sidebar.number_input(f"Iterations for {llm}", min_value=2, value=10, step=1)
-            stability_iterations[llm] = iterations
-else:
-    stability_iterations = {}
-
-# Calculate costs
-results = []
-
-for llm in selected_llms:
-    base_runs = run_counts[llm]
-    stability_runs = stability_iterations.get(llm, 0)
-    total_runs = base_runs * (1 if stability_runs == 0 else stability_runs)
+with left_col:
+    st.header("Configuration")
     
-    total_input_tokens = input_tokens * total_runs
-    total_output_tokens = output_tokens * total_runs
+    # Token input section
+    st.subheader("Token Settings")
+    input_tokens = st.number_input("Input Tokens", min_value=1, value=1000, step=100)
+    output_tokens = st.number_input("Output Tokens", min_value=1, value=500, step=100)
     
-    input_cost = (total_input_tokens / 1_000_000) * llm_data[llm]["input_cost_per_m"]
-    output_cost = (total_output_tokens / 1_000_000) * llm_data[llm]["output_cost_per_m"]
-    total_cost = input_cost + output_cost
+    # LLM selection
+    st.subheader("Select LLMs")
+    selected_llms = st.multiselect("Choose LLMs", options=list(llm_data.keys()), default=list(llm_data.keys()))
     
-    results.append({
-        "Model": llm,
-        "Base Runs": base_runs,
-        "Stability Test Iterations": stability_iterations.get(llm, 0),
-        "Total Runs": total_runs,
-        "Total Input Tokens": total_input_tokens,
-        "Total Output Tokens": total_output_tokens,
-        "Input Cost ($)": input_cost,
-        "Output Cost ($)": output_cost,
-        "Total Cost ($)": total_cost
-    })
-
-# Create DataFrame from results
-results_df = pd.DataFrame(results)
-
-# Main content
-st.header("Cost Summary")
-st.dataframe(results_df, use_container_width=True)
-
-# Calculate overall totals
-total_input_cost = results_df["Input Cost ($)"].sum()
-total_output_cost = results_df["Output Cost ($)"].sum()
-total_cost = results_df["Total Cost ($)"].sum()
-
-# Display totals
-col1, col2, col3 = st.columns(3)
-col1.metric("Total Input Cost", f"${total_input_cost:.2f}")
-col2.metric("Total Output Cost", f"${total_output_cost:.2f}")
-col3.metric("Total API Cost", f"${total_cost:.2f}")
-
-# Data visualization
-st.header("Cost Visualization")
-
-# Cost breakdown by model
-fig1, ax1 = plt.subplots(figsize=(10, 6))
-models = results_df["Model"]
-input_costs = results_df["Input Cost ($)"]
-output_costs = results_df["Output Cost ($)"]
-
-x = np.arange(len(models))
-width = 0.35
-
-ax1.bar(x - width/2, input_costs, width, label='Input Cost')
-ax1.bar(x + width/2, output_costs, width, label='Output Cost')
-
-ax1.set_ylabel('Cost ($)')
-ax1.set_title('Cost Breakdown by Model')
-ax1.set_xticks(x)
-ax1.set_xticklabels(models, rotation=45, ha='right')
-ax1.legend()
-
-fig1.tight_layout()
-st.pyplot(fig1)
-
-# Percentage of total cost by model
-fig2, ax2 = plt.subplots(figsize=(8, 8))
-ax2.pie(results_df["Total Cost ($)"], labels=results_df["Model"], autopct='%1.1f%%', startangle=90)
-ax2.axis('equal')
-ax2.set_title('Percentage of Total Cost by Model')
-st.pyplot(fig2)
-
-# Export options
-st.header("Export Options")
-csv = results_df.to_csv(index=False).encode('utf-8')
-st.download_button(
-    label="Download Results as CSV",
-    data=csv,
-    file_name='llm_budget_results.csv',
-    mime='text/csv',
-)
+    # Run count settings
+    st.subheader("Run Count Settings")
+    uniform_runs = st.checkbox("Run all LLMs the same number of times", value=True)
+    
+    if uniform_runs:
+        uniform_run_count = st.number_input("Number of runs for all LLMs", min_value=1, value=1, step=1)
+        run_counts = {llm: uniform_run_count for llm in selected_llms}
+    else:
+        st.write("Set individual run counts for each LLM:")
+        run_counts = {}
+        for llm in selected_llms:
+            run_counts[llm] = st.number_input(f"Runs for {llm}", min_value=1, value=1, step=1)
+    
+    # Stability test settings
+    st.subheader("Stability Test Settings")
+    stability_test = st.checkbox("Enable stability testing", value=False)
+    
+    stability_iterations = {}
+    if stability_test:
+        st.write("Set stability iterations for selected LLMs:")
+        for llm in selected_llms:
+            stability_enabled = st.checkbox(f"Test stability for {llm}", value=False, key=f"stability_{llm}")
+            if stability_enabled:
+                iterations = st.number_input(f"Iterations for {llm}", min_value=2, value=10, step=1, key=f"iterations_{llm}")
+                stability_iterations[llm] = iterations
+
+with right_col:
+    # Calculate costs
+    st.header("Cost Results")
+    
+    if not selected_llms:
+        st.warning("Please select at least one LLM model.")
+    else:
+        results = []
+        
+        for llm in selected_llms:
+            base_runs = run_counts[llm]
+            stability_runs = stability_iterations.get(llm, 0)
+            total_runs = base_runs * (1 if stability_runs == 0 else stability_runs)
+            
+            total_input_tokens = input_tokens * total_runs
+            total_output_tokens = output_tokens * total_runs
+            
+            input_cost = (total_input_tokens / 1_000_000) * llm_data[llm]["input_cost_per_m"]
+            output_cost = (total_output_tokens / 1_000_000) * llm_data[llm]["output_cost_per_m"]
+            total_cost = input_cost + output_cost
+            
+            results.append({
+                "Model": llm,
+                "Base Runs": base_runs,
+                "Stability Test Iterations": stability_iterations.get(llm, 0),
+                "Total Runs": total_runs,
+                "Total Input Tokens": total_input_tokens,
+                "Total Output Tokens": total_output_tokens,
+                "Input Cost ($)": input_cost,
+                "Output Cost ($)": output_cost,
+                "Total Cost ($)": total_cost
+            })
+        
+        # Create DataFrame from results
+        results_df = pd.DataFrame(results)
+        
+        # Display results
+        st.subheader("Cost Breakdown")
+        st.dataframe(results_df, use_container_width=True)
+        
+        # Calculate overall totals
+        total_input_cost = results_df["Input Cost ($)"].sum()
+        total_output_cost = results_df["Output Cost ($)"].sum()
+        total_cost = results_df["Total Cost ($)"].sum()
+        
+        # Display totals
+        col1, col2, col3 = st.columns(3)
+        col1.metric("Total Input Cost", f"${total_input_cost:.2f}")
+        col2.metric("Total Output Cost", f"${total_output_cost:.2f}")
+        col3.metric("Total API Cost", f"${total_cost:.2f}")
+        
+        # Export options
+        st.subheader("Export Options")
+        csv = results_df.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="Download Results as CSV",
+            data=csv,
+            file_name='llm_budget_results.csv',
+            mime='text/csv',
+        )
 
 # Footer
 st.markdown("---")