Update app.py

app.py

@@ -1,22 +1,89 @@
-# app.py
-import streamlit as st
+# run.py
+import subprocess
+import sys
+import os
+
+def main():
+    # Start Streamlit server only
+    port = int(os.environ.get("PORT", 7860))  # Hugging Face Spaces uses port 7860
+    streamlit_process = subprocess.Popen([
+        sys.executable, 
+        "-m", 
+        "streamlit", 
+        "run", 
+        "app.py",
+        "--server.port", 
+        str(port),
+        "--server.address", 
+        "0.0.0.0"
+    ])
+    
+    try:
+        streamlit_process.wait()
+    except KeyboardInterrupt:
+        streamlit_process.terminate()
+
+if __name__ == "__main__":
+    main()
+
+# api.py
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from typing import List
+from app import translate_text
+
+app = FastAPI()
+
+class InputData(BaseModel):
+    sentences: List[str]
+    target_lang: str
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+@app.post("/translate")
+async def translate(input_data: InputData):
+    try:
+        result = translate_text(
+            sentences=input_data.sentences,
+            target_lang=input_data.target_lang
+        )
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# app.py
+import streamlit as st
+from fastapi import FastAPI
+from typing import List
 import torch
-import asyncio
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from IndicTransToolkit import IndicProcessor
-import requests
 import json
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+import uvicorn
+
+# Initialize FastAPI
+api = FastAPI()
+
+# Add CORS middleware
+api.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",
+    "ai4bharat/indictrans2-en-indic-1B", 
     trust_remote_code=True
 )
 tokenizer = AutoTokenizer.from_pretrained(
-    "ai4bharat/indictrans2-en-indic-1B",
+    "ai4bharat/indictrans2-en-indic-1B", 
     trust_remote_code=True
 )
 ip = IndicProcessor(inference=True)
@@ -31,7 +98,6 @@ def translate_text(sentences: List[str], target_lang: str):
             src_lang=src_lang,
             tgt_lang=target_lang
         )
-        
         inputs = tokenizer(
             batch,
             truncation=True,
@@ -58,16 +124,27 @@ def translate_text(sentences: List[str], target_lang: str):
             )
             
         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
+@api.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+@api.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))
+
 # Streamlit interface
 def main():
     st.title("Indic Language Translator")
@@ -100,11 +177,8 @@ def main():
                 sentences=[text_input],
                 target_lang=target_languages[target_lang]
             )
-            
-            # Display result
             st.success("Translation:")
             st.write(result["translations"][0])
-            
         except Exception as e:
             st.error(f"Translation failed: {str(e)}")
 
@@ -116,7 +190,6 @@ def main():
     ```
     https://USERNAME-SPACE_NAME.hf.space/translate
     ```
-    
     Request body format:
     ```json
     {
@@ -124,19 +197,21 @@ def main():
         "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`
     """)
+    st.markdown("Available target languages:")
+    for lang, code in target_languages.items():
+        st.markdown(f"- {lang}: `{code}`")
 
 if __name__ == "__main__":
+    # Run both Streamlit and FastAPI
+    import threading
+    
+    def run_fastapi():
+        uvicorn.run(api, host="0.0.0.0", port=8000)
+    
+    # Start FastAPI in a separate thread
+    api_thread = threading.Thread(target=run_fastapi)
+    api_thread.start()
+    
+    # Run Streamlit
     main()