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import gradio as gr
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
# Load the new model and tokenizer
context_model = SentenceTransformer("all-MiniLM-L6-v2")
# Define your labels
labels = [
"aerospace", "anatomy", "anthropology", "art",
"automotive", "blockchain", "biology", "chemistry",
"cryptocurrency", "data science", "design", "e-commerce",
"education", "engineering", "entertainment", "environment",
"fashion", "finance", "food commerce", "general",
"gaming", "healthcare", "history", "html",
"information technology", "IT", "keywords", "legal",
"literature", "machine learning", "marketing", "medicine",
"music", "personal development", "philosophy", "physics",
"politics", "poetry", "programming", "real estate", "retail",
"robotics", "slang", "social media", "speech", "sports",
"sustained", "technical", "theater", "tourism", "travel"
]
# Pre-compute label embeddings
label_embeddings = context_model.encode(labels)
def detect_context(input_text, top_n=3, score_threshold=0.05):
# Encode input text
input_embedding = context_model.encode([input_text])
# Compute cosine similarity with labels
similarities = cosine_similarity(input_embedding, label_embeddings)[0]
# Pair labels with scores
label_scores = [(label, score) for label, score in zip(labels, similarities)]
# Sort by score and filter by threshold
sorted_labels = sorted(label_scores, key=lambda x: x[1], reverse=True)
filtered_labels = [label for label, score in sorted_labels if score > score_threshold]
# Return top N contexts
return filtered_labels[:top_n] if filtered_labels else ["general"]
# Translation client for space_3
from gradio_client import Client
translation_client = Client("Frenchizer/space_3") # Replace with your Space name
def translate_text(input_text):
# Call the translation model
result = translation_client.predict(input_text)
return result
def process_request(input_text):
# Detect context
context = detect_context(input_text)
print(f"Detected context: {context}")
# Translate text
translation = translate_text(input_text)
return translation
# Create a Gradio interface
interface = gr.Interface(
fn=process_request,
inputs="text",
outputs="text",
title="Frenchizer",
description="Translate text from English to French with context detection."
)
# Launch the Gradio app
interface.launch()
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