Create app.py

app.py

@@ -0,0 +1,39 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the data from the CSV file
+@st.cache
+def load_data():
+    df = pd.read_csv("llm_data.csv")  # Update with your CSV file path
+    return df
+
+df = load_data()
+
+# Calculate example cost
+def calculate_example_cost(input_text, output_text, input_ratio=0.000001, output_ratio=0.000001):
+    input_tokens = len(input_text) / 5
+    output_tokens = len(output_text) / 5
+    example_cost = (input_tokens * input_ratio) + (output_tokens * output_ratio)
+    return example_cost
+
+# Sidebar inputs
+input_text = st.sidebar.text_area("Input text")
+output_text = st.sidebar.text_area("Output text")
+
+# Calculate example cost for each row
+df['Example cost'] = df.apply(lambda row: calculate_example_cost(input_text, output_text, row['Input'], row['Output']), axis=1)
+
+# Display sorted LLM costs
+st.write("Sorted LLM Costs:")
+sorted_df = df.sort_values(by='Example cost', ascending=False)
+st.write(sorted_df[['Company', 'Model', 'Example cost']])
+
+# Plot visualization
+st.write("Visualization of LLM Costs:")
+plt.figure(figsize=(10, 6))
+plt.barh(sorted_df['Model'], sorted_df['Example cost'], color='skyblue')
+plt.xlabel('Example Cost')
+plt.ylabel('LLM Model')
+plt.title('LLM Usage Cost')
+st.pyplot(plt)