app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import torch
import numpy as np
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()

# Precompute label embeddings
labels = [
    "aerospace", "anatomy", "anthropology", "art", 
    "automotive", "blockchain", "biology", "chemistry", 
    "cryptocurrency", "data science", "design", "e-commerce",
    "education", "engineering", "entertainment", "environment",
    "fashion", "finance", "food commerce", "gaming",
    "healthcare", "history", "information technology", 
    "legal", "machine learning", "marketing", "medicine",
    "music", "philosophy", "physics", "politics", "real estate", "retail", 
    "robotics", "social media", "sports", "technical",
    "tourism", "travel"
]

tones = [
    "formal", "positive", "negative", "poetic", "polite", "subtle", "casual", "neutral",
    "informal", "pompous", "sustained", "rude", "sustained",
    "sophisticated", "playful", "serious", "friendly"
]

styles = [
    "poetry", "novel", "theater", "slang", "speech", "keywords", "html", "programming"
]

gender_number = [
    "masculine singular", "masculine plural", "feminine singular", "feminine plural"
]

@lru_cache(maxsize=1)
def precompute_label_embeddings():
    inputs = tokenizer(labels, padding=True, truncation=True, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).numpy()  # Mean pooling for embeddings

label_embeddings = precompute_label_embeddings()

# Softmax function to convert scores to probabilities
def softmax(x):
    exp_x = np.exp(x - np.max(x))  # Subtract max for numerical stability
    return exp_x / exp_x.sum()

# Function to detect context
def detect_context(input_text, threshold=0.03):
    # Encode the input text
    inputs = tokenizer([input_text], padding=True, truncation=True, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    input_embedding = outputs.last_hidden_state.mean(dim=1).numpy()  # Mean pooling for embedding

    # Compute cosine similarities
    similarities = cosine_similarity(input_embedding, label_embeddings)[0]

    # Apply softmax to convert similarities to probabilities
    probabilities = softmax(similarities)

    # Pair each label with its probability
    label_probabilities = list(zip(labels, probabilities))

    # Filter contexts with confidence >= threshold
    high_confidence_contexts = [(label, score) for label, score in label_probabilities if score >= threshold]

    # If no contexts meet the threshold, default to "general"
    if not high_confidence_contexts:
        high_confidence_contexts = [("general", 1.0)]  # Assign a default score of 1.0 for "general"

    return high_confidence_contexts

# Mock translation clients for different contexts
def get_translation_client(context):
    """
    Returns the appropriate Hugging Face Space client for the given context.
    For now, all contexts use the same mock space.
    """
    return Client("Frenchizer/space_18")  # Replace with actual Space paths for each context

def translate_text(input_text, context):
    """
    Translates the input text using the appropriate model for the given context.
    """
    client = get_translation_client(context)
    return client.predict(input_text)

def process_request(input_text):
    # Step 1: Detect context
    context_results = detect_context(input_text)

    # Step 2: Translate the text for each context
    translations = {}
    for context, score in context_results:
        translations[context] = translate_text(input_text, context)

    # Step 3: Print the list of high-confidence contexts and translations
    print("High-confidence contexts (score >= 0.022):", context_results)
    print("Translations:", translations)

    # Return the translations and contexts
    return translations, context_results

def gradio_interface(input_text):
    # Call process_request to get translations and context_results
    translations, contexts = process_request(input_text)
    
    # Extract only the translation values from the dictionary
    translation_values = list(translations.values())
    
    # Join the translations into a single string with line breaks
    output = "\n".join(translation_values)
    
    return output.strip()

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

interface.launch()