add apis

Dockerfile

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+# Use the official Python 3.10.9 image
+FROM python:3.10.9
+
+# Copy the current directory contents into the container at .
+COPY . .
+
+# Set the working directory to /
+WORKDIR /
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+# Start the FastAPI app on port 7860, the default port expected by Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from typing import List
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from IndicTransToolkit import IndicProcessor
+from fastapi.middleware.cors import CORSMiddleware
+
+# Initialize FastAPI
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize models and processors
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True
+)
+ip = IndicProcessor(inference=True)
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(DEVICE)
+
+
+def translate_text(sentences: List[str], target_lang: str):
+    try:
+        src_lang = "eng_Latn"
+        batch = ip.preprocess_batch(sentences, src_lang=src_lang, tgt_lang=target_lang)
+        inputs = tokenizer(
+            batch,
+            truncation=True,
+            padding="longest",
+            return_tensors="pt",
+            return_attention_mask=True,
+        ).to(DEVICE)
+
+        with torch.no_grad():
+            generated_tokens = model.generate(
+                **inputs,
+                use_cache=True,
+                min_length=0,
+                max_length=256,
+                num_beams=5,
+                num_return_sequences=1,
+            )
+
+        with tokenizer.as_target_tokenizer():
+            generated_tokens = tokenizer.batch_decode(
+                generated_tokens.detach().cpu().tolist(),
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=True,
+            )
+
+        translations = ip.postprocess_batch(generated_tokens, lang=target_lang)
+        return {
+            "translations": translations,
+            "source_language": src_lang,
+            "target_language": target_lang,
+        }
+    except Exception as e:
+        raise Exception(f"Translation failed: {str(e)}")
+
+
+# FastAPI routes
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+
+@app.post("/translate")
+async def translate_endpoint(sentences: List[str], target_lang: str):
+    try:
+        result = translate_text(sentences=sentences, target_lang=target_lang)
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))