Upload folder using huggingface_hub

.gitattributes

@@ -1,35 +1,35 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,14 @@
----
-title: Text To Dollars
-emoji: 🦀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Text-To-Dollars
+emoji: 💰
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 4.43.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Get the price for any API LLM call.
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,315 @@
+import gradio as gr
+import pandas as pd
+import requests
+import json
+import tiktoken
+import matplotlib.pyplot as plt
+
+# Constants
+USD_TO_INR = 84
+PRICES_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+
+# Fetch and process token costs
+try:
+    response = requests.get(PRICES_URL)
+    if response.status_code == 200:
+        TOKEN_COSTS = response.json()
+    else:
+        raise Exception(f"Failed to fetch token costs, status code: {response.status_code}")
+except Exception as e:
+    print(f'Failed to update token costs with error: {e}. Using static costs.')
+    with open("model_prices.json", "r") as f:
+        TOKEN_COSTS = json.load(f)
+
+TOKEN_COSTS = pd.DataFrame.from_dict(TOKEN_COSTS, orient='index').reset_index()
+TOKEN_COSTS.columns = ['model'] + list(TOKEN_COSTS.columns[1:])
+TOKEN_COSTS = TOKEN_COSTS.loc[
+    (~TOKEN_COSTS["model"].str.contains("sample_spec"))
+    & (~TOKEN_COSTS["input_cost_per_token"].isnull())
+    & (~TOKEN_COSTS["output_cost_per_token"].isnull())
+    & (TOKEN_COSTS["input_cost_per_token"] > 0)
+    & (TOKEN_COSTS["output_cost_per_token"] > 0)
+]
+TOKEN_COSTS["supports_vision"] = TOKEN_COSTS["supports_vision"].fillna(False)
+
+# Convert USD costs to INR
+TOKEN_COSTS["input_cost_per_token"] *= USD_TO_INR
+TOKEN_COSTS["output_cost_per_token"] *= USD_TO_INR
+
+def clean_names(s):
+    s = s.replace("_", " ").replace("ai", "AI")
+    return s[0].upper() + s[1:]
+
+TOKEN_COSTS["litellm_provider"] = TOKEN_COSTS["litellm_provider"].apply(clean_names)
+
+cmap = plt.get_cmap('RdYlGn_r')  # Red-Yellow-Green colormap, reversed
+
+def count_string_tokens(string: str, model: str) -> int:
+    try:
+        encoding = tiktoken.encoding_for_model(model.split('/')[-1])
+    except:
+        if len(model.split('/')) > 1:
+            try:
+                encoding = tiktoken.encoding_for_model(model.split('/')[-2] + '/' + model.split('/')[-1])
+            except KeyError:
+                print(f"Model {model} not found. Using cl100k_base encoding.")
+                encoding = tiktoken.get_encoding("cl100k_base")
+        else:
+            print(f"Model {model} not found. Using cl100k_base encoding.")
+            encoding = tiktoken.get_encoding("cl100k_base")
+    return len(encoding.encode(string))
+
+def calculate_total_cost(prompt_tokens: int, completion_tokens: int, model: str) -> float:
+    model_data = TOKEN_COSTS[TOKEN_COSTS['model'] == model].iloc[0]
+    prompt_cost = prompt_tokens * model_data['input_cost_per_token']
+    
+    
+    
+
+   
+    completion_cost = completion_tokens * model_data['output_cost_per_token']
+    
+    return prompt_cost, completion_cost
+
+def update_model_list(function_calling, litellm_provider, max_price, supports_vision, supports_max_input_tokens):
+    filtered_models = TOKEN_COSTS.loc[TOKEN_COSTS["max_input_tokens"] >= supports_max_input_tokens*1000]
+
+    if litellm_provider != "Any":
+        filtered_models = filtered_models[filtered_models['litellm_provider'] == litellm_provider]
+    
+    if supports_vision:
+        filtered_models = filtered_models[filtered_models['supports_vision']]
+    
+    list_models = filtered_models['model'].tolist()
+    return gr.Dropdown(choices=list_models, value=list_models[0] if list_models else "No model found for this combination!")
+
+
+
+
+
+def compute_all(input_type, prompt_text, completion_text, prompt_tokens, completion_tokens, models):
+    results = []
+    temp=prompt_tokens
+    temp2=completion_tokens
+    for model in models:
+        if input_type == "Text Input":
+            prompt_tokens = count_string_tokens(prompt_text, model)
+            completion_tokens = count_string_tokens(completion_text, model)
+        else:  # Token Count Input
+            
+            
+            prompt_tokens= int(prompt_tokens * 1000)
+
+            completion_tokens = int(completion_tokens * 1000)
+
+        model_data = TOKEN_COSTS[TOKEN_COSTS['model'] == model].iloc[0]
+        prompt_cost, completion_cost = calculate_total_cost(prompt_tokens, completion_tokens, model)
+        
+        total_cost = prompt_cost + completion_cost
+              
+        results.append({
+            "Model": model,
+            "Provider": model_data['litellm_provider'],
+            "Input Cost / M tokens": model_data['input_cost_per_token']*1e6,
+            "Output Cost / M tokens": model_data['output_cost_per_token']*1e6,
+            "Total Cost": round(total_cost, 2),
+        })
+        prompt_tokens=temp
+        completion_tokens=temp2
+        
+    
+    df = pd.DataFrame(results)
+
+    if len(df) > 1:
+        norm = plt.Normalize(df['Total Cost'].min(), df['Total Cost'].max())
+        
+        def get_color(val):
+            color = cmap(norm(val))
+            return f'rgba({int(color[0]*255)}, {int(color[1]*255)}, {int(color[2]*255)}, 0.3)'
+        
+    else:
+        def get_color(val):
+            return "rgba(0, 0, 0, 0)"
+    
+    # Create the HTML table with animations
+    html_table = '<table class="styled-table">'
+    html_table += '<thead><tr>'
+    for col in df.columns:
+        html_table += f'<th>{col}</th>'
+    html_table += '</tr></thead><tbody>'
+    
+    for i, row in df.iterrows():
+        html_table += f'<tr class="animate-row" style="animation-delay: {i * 0.1}s;">'
+        for col in df.columns:
+            value = row[col]
+            if col == 'Total Cost':
+                color = get_color(value)
+                html_table += f'<td class="total-cost" style="background-color: {color};">₹{value:.2f}</td>'
+            elif col in ["Input Cost / M tokens", "Output Cost / M tokens"]:
+                html_table += f'<td>₹{value:.2f}</td>'
+            else:
+                html_table += f'<td>{value}</td>'
+        html_table += '</tr>'
+    
+    html_table += '</tbody></table>'
+    
+    return html_table
+
+def toggle_input_visibility(choice):
+    return (
+        gr.Group(visible=(choice == "Text Input")),
+        gr.Group(visible=(choice == "Token Count Input"))
+    )
+
+with gr.Blocks(css="""
+    .styled-table {
+        border-collapse: separate;
+        border-spacing: 0;
+        margin: 25px 0;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        width: 100%;
+        box-shadow: 0 0 20px rgba(0, 0, 0, 0.1);
+        border-radius: 12px;
+        overflow: hidden;
+        background-color: #f8f9fa;
+    }
+    .styled-table thead tr {
+        background-color: #3a506b;
+        color: #ffffff;
+        text-align: left;
+        font-weight: bold;
+    }
+    .styled-table th,
+    .styled-table td {
+        padding: 14px 18px;
+        border-bottom: 1px solid #e0e0e0;
+    }
+    .styled-table tbody tr {
+        transition: all 0.3s ease;
+    }
+    .styled-table tbody tr:nth-of-type(even) {
+        background-color: #f0f4f8;
+    }
+    .styled-table tbody tr:last-of-type {
+        border-bottom: 2px solid #3a506b;
+    }
+    .styled-table tbody tr:hover {
+        background-color: #e3e8ef;
+        transform: scale(1.02);
+        box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    }
+    .total-cost {
+        font-weight: bold;
+        transition: all 0.3s ease;
+        color: #2c3e50;
+    }
+    .total-cost:hover {
+        transform: scale(1.1);
+        color: #e74c3c;
+    }
+    @keyframes fadeIn {
+        from { opacity: 0; transform: translateY(20px); }
+        to { opacity: 1; transform: translateY(0); }
+    }
+    .animate-row {
+        animation: fadeIn 0.5s ease-out forwards;
+        opacity: 0;
+    }
+    .styled-table tbody tr td {
+        color: #34495e;
+    }
+    .styled-table tbody tr:hover td {
+        color: #2c3e50;
+    }
+""", theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.slate)) as demo:
+    gr.Markdown("""
+    # 💰 Text-to-Rupees: Get the price of your LLM API calls in INR! 💰
+    Based on prices data from [BerriAI's litellm](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
+    Prices converted to INR (1 USD = 84 INR).
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Input type:")
+            input_type = gr.Radio(["Text Input", "Token Count Input"], label="Input Type", value="Text Input")
+            
+            with gr.Group() as text_input_group:
+                prompt_text = gr.Textbox(label="Prompt", value="Tell me a joke about AI.", lines=3)
+                completion_text = gr.Textbox(label="Completion", value="Certainly: Why did the neural network go to therapy? It had too many deep issues!", lines=3)
+            
+            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)
+
+        with gr.Column():
+            gr.Markdown("## Model choice:")
+            with gr.Row():
+                with gr.Column():
+                    function_calling = gr.Checkbox(label="Supports Tool Calling", value=False)
+                    supports_vision = gr.Checkbox(label="Supports Vision", value=False)
+                with gr.Column():
+                    supports_max_input_tokens = gr.Slider(label="Min Supported Input Length (thousands)", minimum=2, maximum=256, step=2, value=2)
+                    max_price = gr.Slider(label="Max Price per Input Token", minimum=0, maximum=0.084, step=0.00084, value=0.084, visible=False, interactive=False)
+                litellm_provider = gr.Dropdown(label="Inference Provider", choices=["Any"] + TOKEN_COSTS['litellm_provider'].unique().tolist(), value="Any")
+        
+            model = gr.Dropdown(label="Models (at least 1)", choices=TOKEN_COSTS['model'].tolist(), value=["anyscale/meta-llama/Meta-Llama-3-8B-Instruct", "gpt-4o", "claude-3-sonnet-20240229"], multiselect=True)
+        
+    gr.Markdown("## Resulting Costs 👇")
+
+    with gr.Row():
+        results_table = gr.HTML()
+
+    input_type.change(
+        toggle_input_visibility,
+        inputs=[input_type],
+        outputs=[text_input_group, token_input_group]
+    )
+
+    gr.on(
+        triggers=[function_calling.change, litellm_provider.change, max_price.change, supports_vision.change, supports_max_input_tokens.change],
+        fn=update_model_list,
+        inputs=[function_calling, litellm_provider, max_price, supports_vision, supports_max_input_tokens],
+        outputs=model,
+    )
+
+    gr.on(
+        triggers=[
+            input_type.change,
+            prompt_text.change,
+            completion_text.change,
+            prompt_tokens_input.change,
+            completion_tokens_input.change,
+            function_calling.change,
+            litellm_provider.change,
+            supports_vision.change,
+            supports_max_input_tokens.change,
+            model.change
+        ],
+        fn=compute_all,
+        inputs=[
+            input_type,
+            prompt_text,
+            completion_text,
+            prompt_tokens_input,
+            completion_tokens_input,
+            model
+        ],
+        outputs=results_table
+    )
+
+    # Load results on page load
+    demo.load(
+        fn=compute_all,
+        inputs=[
+            input_type,
+            prompt_text,
+            completion_text,
+            prompt_tokens_input,
+            completion_tokens_input,
+            model
+        ],
+        outputs=results_table
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+pandas
+tiktoken
+matplotlib