Update app.py

app.py

@@ -7,12 +7,8 @@ import torch
 import asyncio
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from IndicTransToolkit import IndicProcessor
-import uvicorn
-import nest_asyncio
-import threading
-
-# Initialize FastAPI
-app = FastAPI()
+import requests
+import json
 
 # Initialize models and processors
 model = AutoModelForSeq2SeqLM.from_pretrained(
@@ -27,25 +23,13 @@ ip = IndicProcessor(inference=True)
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 model = model.to(DEVICE)
 
-class InputData(BaseModel):
-    sentences: List[str]
-    target_lang: str
-
-# FastAPI endpoints
-@app.get("/health")
-async def health_check():
-    return {"status": "healthy"}
-
-@app.post("/translate/")
-async def translate(input_data: InputData):
+def translate_text(sentences: List[str], target_lang: str):
     try:
         src_lang = "eng_Latn"
-        tgt_lang = input_data.target_lang
-        
         batch = ip.preprocess_batch(
-            input_data.sentences,
+            sentences,
             src_lang=src_lang,
-            tgt_lang=tgt_lang
+            tgt_lang=target_lang
         )
         
         inputs = tokenizer(
@@ -73,16 +57,16 @@ async def translate(input_data: InputData):
                 clean_up_tokenization_spaces=True
             )
             
-        translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang)
+        translations = ip.postprocess_batch(generated_tokens, lang=target_lang)
         
         return {
             "translations": translations,
             "source_language": src_lang,
-            "target_language": tgt_lang
+            "target_language": target_lang
         }
         
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise Exception(f"Translation failed: {str(e)}")
 
 # Streamlit interface
 def main():
@@ -112,18 +96,11 @@ def main():
     
     if st.button("Translate"):
         try:
-            # Prepare input data
-            input_data = InputData(
+            result = translate_text(
                 sentences=[text_input],
                 target_lang=target_languages[target_lang]
             )
             
-            # Create event loop and run translation
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            result = loop.run_until_complete(translate(input_data))
-            loop.close()
-            
             # Display result
             st.success("Translation:")
             st.write(result["translations"][0])
@@ -131,14 +108,35 @@ def main():
         except Exception as e:
             st.error(f"Translation failed: {str(e)}")
 
-def run_fastapi():
-    nest_asyncio.apply()
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    # Add API documentation
+    st.markdown("---")
+    st.header("API Documentation")
+    st.markdown("""
+    To use the translation API, send POST requests to:
+    ```
+    https://USERNAME-SPACE_NAME.hf.space/translate
+    ```
+    
+    Request body format:
+    ```json
+    {
+        "sentences": ["Your text here"],
+        "target_lang": "hin_Deva"
+    }
+    ```
+    
+    Available target languages:
+    - Hindi: `hin_Deva`
+    - Bengali: `ben_Beng`
+    - Tamil: `tam_Taml`
+    - Telugu: `tel_Telu`
+    - Marathi: `mar_Deva`
+    - Gujarati: `guj_Gujr`
+    - Kannada: `kan_Knda`
+    - Malayalam: `mal_Mlym`
+    - Punjabi: `pan_Guru`
+    - Odia: `ori_Orya`
+    """)
 
 if __name__ == "__main__":
-    # Start FastAPI in a separate thread
-    api_thread = threading.Thread(target=run_fastapi, daemon=True)
-    api_thread.start()
-    
-    # Run Streamlit interface
     main()