app.py

import gradio as gr
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from huggingface_hub import hf_hub_download
import joblib
import random
import os

# Function to load the model from HuggingFace
def load_model_from_hf(model_id="jersonalvr/random"):
    # Create a cache directory
    cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "random_number_generator")
    os.makedirs(cache_dir, exist_ok=True)

    # Download tokenizer
    tokenizer = DistilBertTokenizer.from_pretrained(model_id, cache_dir=cache_dir)
    
    # Download model
    model = DistilBertForSequenceClassification.from_pretrained(model_id, cache_dir=cache_dir)
    
    # Download label encoder
    try:
        label_encoder_path = hf_hub_download(
            repo_id=model_id, 
            filename="label_encoder.joblib",
            cache_dir=cache_dir
        )
        label_encoder = joblib.load(label_encoder_path)
    except Exception as e:
        print(f"Error downloading label encoder: {e}")
        # Fallback: create a basic label encoder
        from sklearn.preprocessing import LabelEncoder
        label_encoder = LabelEncoder()
        label_encoder.classes_ = ['generar_numero_unico', 'generar_numero_digitos', 'generar_numeros_rango', 'generar_numeros_sin_rango']
    
    return model, tokenizer, label_encoder

# Function to predict intent
def predict_intent(prompt, model, tokenizer, label_encoder):
    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        max_length=32,
        truncation=True,
        padding='max_length'
    )

    with torch.no_grad():
        logits = model(**inputs).logits
    
    pred_id = torch.argmax(logits, dim=1).item()
    intent = label_encoder.inverse_transform([pred_id])[0]
    return intent

# Intelligent parameter extraction
def extract_parameters(prompt):
    # Basic parameter extraction logic
    params = {
        "count": 1,
        "min": 0,
        "max": 9999
    }

    # Look for number of numbers
    if "un número" in prompt or "un numero" in prompt:
        params["count"] = 1
    elif any(word in prompt for word in ["2 números", "2 numeros", "dos números", "dos numeros"]):
        params["count"] = 2
    elif any(word in prompt for word in ["3 números", "3 numeros", "tres números", "tres numeros"]):
        params["count"] = 3
    elif any(word in prompt for word in ["4 números", "4 numeros", "cuatro números", "cuatro numeros"]):
        params["count"] = 4
    elif any(word in prompt for word in ["5 números", "5 numeros", "cinco números", "cinco numeros"]):
        params["count"] = 5
    elif any(word in prompt for word in ["10 números", "10 numeros", "diez números", "diez numeros"]):
        params["count"] = 10

    # Look for specific ranges
    ranges = [
        (0, 9, "un dígito", "un digito"),
        (10, 99, "dos dígitos", "dos digitos"),
        (100, 999, "tres dígitos", "tres digitos"),
        (1000, 9999, "cuatro dígitos", "cuatro digitos"),
    ]

    for min_val, max_val, *range_words in ranges:
        if any(word in prompt.lower() for word in range_words):
            params["min"] = min_val
            params["max"] = max_val
            break

    # Custom range extraction
    import re
    range_match = re.search(r'entre\s+(-?\d+)\s+y\s+(-?\d+)', prompt.lower())
    if range_match:
        params["min"] = int(range_match.group(1))
        params["max"] = int(range_match.group(2))

    return params

# Function to generate numbers
def generate_numbers(intent_params, distinct=False):
    count = intent_params["count"]
    min_val = intent_params["min"]
    max_val = intent_params["max"]
    
    # Handle distinct numbers case
    if distinct and count <= (max_val - min_val + 1):
        return random.sample(range(min_val, max_val + 1), count)
    else:
        return [random.randint(min_val, max_val) for _ in range(count)]

# Predefined example prompts
EXAMPLE_PROMPTS = [
    "Dame un número de dos dígitos",
    "Genera 3 números entre 1 y 10",
    "Necesito un número aleatorio",
    "Dame 5 números de tres dígitos",
    "Quiero 2 números entre 100 y 200"
]

def number_generator(prompt, distinct):
    # Load model and utilities
    model, tokenizer, label_encoder = load_model_from_hf()
    
    # Predict intent
    intent = predict_intent(prompt, model, tokenizer, label_encoder)
    
    # Extract parameters intelligently
    intent_params = extract_parameters(prompt)
    
    # Generate numbers
    numbers = generate_numbers(intent_params, distinct)
    
    return {
        "Prompt": prompt,
        "Intent": intent,
        "Parameters": intent_params,
        "Generated Numbers": numbers
    }

# Create Gradio interface
def create_gradio_app():
    iface = gr.Interface(
        fn=number_generator,
        inputs=[
            gr.Textbox(label="Enter your prompt"),
            gr.Checkbox(label="Distinct Numbers", value=False)
        ],
        outputs=[
            gr.JSON(label="Result"),
        ],
        title="Random Number Generator with Intent Classification",
        description="Generate numbers based on your natural language prompt",
        examples=[[prompt, False] for prompt in EXAMPLE_PROMPTS],
        theme="default"
    )
    
    return iface

# Launch the app
if __name__ == "__main__":
    app = create_gradio_app()
    app.launch(share=True)