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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, pipeline
+import numpy as np
+
+MODEL_FILE = "./model.onnx"
+session = ort.InferenceSession(MODEL_FILE)
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
+
+# Predefined labels for context detection
+labels = [
+    "general", "pharma", "legal", "technical", "UI", "medicine", "it", "marketing", 
+    "e-commerce", "programming", "website", "html", "keywords", "food commerce", 
+    "personal development", "literature", "poetry", "physics", "chemistry", "biology", 
+    "theater", "finance", "sports", "education", "politics", "economics", "art", 
+    "history", "music", "gaming", "aerospace", "engineering", "robotics", "travel", 
+    "tourism", "healthcare", "psychology", "environment", "fashion", "design", 
+    "real estate", "retail", "news", "entertainment", "social media", "automotive", 
+    "machine learning", "cryptocurrency", "blockchain", "philosophy", "anthropology", 
+    "archaeology", "data science"
+]
+
+# Context detection pipeline
+context_pipeline = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+def detect_context(input_text):
+    result = context_pipeline(input_text, candidate_labels=labels)
+    return result["labels"][0]
+
+def gradio_predict(input_text):
+    try:
+        tokenized_input = tokenizer(
+            input_text, return_tensors="np", padding=True, truncation=True, max_length=512
+        )
+        input_ids = tokenized_input["input_ids"].astype(np.int64)
+        attention_mask = tokenized_input["attention_mask"].astype(np.int64)
+
+        decoder_start_token_id = tokenizer.cls_token_id or tokenizer.pad_token_id
+        decoder_input_ids = np.array([[decoder_start_token_id]], dtype=np.int64)
+
+        for _ in range(512):
+            outputs = session.run(
+                None,
+                {
+                    "input_ids": input_ids,
+                    "attention_mask": attention_mask,
+                    "decoder_input_ids": decoder_input_ids,
+                }
+            )
+
+            logits = outputs[0]
+            next_token_id = np.argmax(logits[:, -1, :], axis=-1).item()
+            decoder_input_ids = np.concatenate(
+                [decoder_input_ids, np.array([[next_token_id]], dtype=np.int64)], axis=1
+            )
+
+            if next_token_id == tokenizer.eos_token_id:
+                break
+
+        translated_text = tokenizer.decode(decoder_input_ids[0], skip_special_tokens=True)
+
+        return {
+            "translation": translated_text,
+            "context": detect_context(input_text)
+        }
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+gr.Interface(
+    fn=gradio_predict,
+    inputs="text",
+    outputs=["text", "text"],
+    live=True
+).launch()

requirements.txt

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+fastapi
+uvicorn
+onnxruntime
+transformers
+numpy
+pydantic
+requests
+gradio
+sentencepiece
+sacremoses