app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModel
import torch
from gradio_client import Client
from functools import lru_cache

# Cache the model and tokenizer using lru_cache
@lru_cache(maxsize=1)
def load_model_and_tokenizer():
    model_name = "./all-MiniLM-L6-v2"  # Replace with your Space and model path
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModel.from_pretrained(model_name)
    return tokenizer, model

# Load the model and tokenizer
tokenizer, model = load_model_and_tokenizer()

# Function to detect context (simplified)
def detect_context(input_text):
    # Tokenize the input text
    inputs = tokenizer([input_text], padding=True, truncation=True, return_tensors="pt")
    
    # Run the model
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get the embedding (mean pooling)
    input_embedding = outputs.last_hidden_state.mean(dim=1).numpy()
    
    # For now, return a placeholder context
    # You can replace this with a more sophisticated logic if needed
    return ["general"]

# Translation client
translation_client = Client("Frenchizer/space_3")

def translate_text(input_text):
    return translation_client.predict(input_text)

def process_request(input_text):
    context = detect_context(input_text)
    print(f"Detected context: {context}")
    translation = translate_text(input_text)
    return translation

# Gradio interface
interface = gr.Interface(
    fn=process_request,
    inputs="text",
    outputs="text",
    title="Frenchizer",
    description="Translate text from English to French with context detection."
)

interface.launch()