added glossController

app.py

@@ -6,10 +6,11 @@ from typing import List
 from starlette.responses import JSONResponse
 import tempfile
 
-# Correct import statements for both controllers
+# Correct import statements for all controllers
 try:
     from lettersController import detectFromImage
     from wordsController import detectWords
+    from glossController import translateGloss
     print("Successfully imported functions from controllers.")
 except ImportError as e:
     print(f"ERROR: Could not import from controllers: {e}")
@@ -22,7 +23,7 @@ app = FastAPI()
 # The models are loaded when the controller modules are imported.
 # It's a good practice to print a message to confirm this.
 print("\n--- Hugging Face Space starting. Models are being loaded... ---")
-print("--- AI letter and words models loaded. Ready to serve predictions. ---\n")
+print("--- AI letter, words, and translation models loaded. Ready to serve predictions. ---\n")
 
 # --- FastAPI Route for Letter Detection ---
 @app.post("/detect-letters")
@@ -118,6 +119,27 @@ async def process_words(
             os.remove(path)
         os.rmdir(temp_dir)
 
+# --- FastAPI Route for Sentence Translation ---
+@app.post("/sentence")
+async def process_sentence(
+    gloss: str = Form(...)
+):
+    """
+    Receives a string of gloss and translates it into a human-readable sentence.
+    """
+    try:
+        result = translateGloss(gloss)
+        return JSONResponse(content={"translation": result, "success": True})
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Error translating gloss",
+                "details": str(e),
+                "success": False
+            }
+        )
+
 if __name__ == "__main__":
     # Hugging Face Spaces automatically sets the port to 7860
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)

glossController.py

@@ -0,0 +1,19 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+def translateGloss(gloss: str, model_id: str = "rrrr66254/Glossa-BART") -> str:
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_id, trust_remote_code=True)
+    model.eval()
+    if torch.cuda.is_available():
+        model = model.to("cuda")
+    
+    inputs = tokenizer(gloss, return_tensors="pt", padding=True, truncation=True)
+    if torch.cuda.is_available():
+        inputs = {k: v.to("cuda") for k,v in inputs.items()}
+    
+    outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+