Add apis

app.py

@@ -7,14 +7,13 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from IndicTransToolkit import IndicProcessor
 import json
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
 import uvicorn
 
 # Initialize FastAPI
-api = FastAPI()
+app = FastAPI()
 
 # Add CORS middleware
-api.add_middleware(
+app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
     allow_credentials=True,
@@ -24,33 +23,28 @@ api.add_middleware(
 
 # Initialize models and processors
 model = AutoModelForSeq2SeqLM.from_pretrained(
-    "ai4bharat/indictrans2-en-indic-1B", 
-    trust_remote_code=True
+    "ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True
 )
 tokenizer = AutoTokenizer.from_pretrained(
-    "ai4bharat/indictrans2-en-indic-1B", 
-    trust_remote_code=True
+    "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
-        )
+        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
+            return_attention_mask=True,
         ).to(DEVICE)
-        
+
         with torch.no_grad():
             generated_tokens = model.generate(
                 **inputs,
@@ -58,31 +52,33 @@ def translate_text(sentences: List[str], target_lang: str):
                 min_length=0,
                 max_length=256,
                 num_beams=5,
-                num_return_sequences=1
+                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
+                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
+            "target_language": target_lang,
         }
     except Exception as e:
         raise Exception(f"Translation failed: {str(e)}")
 
+
 # FastAPI routes
-@api.get("/health")
+@app.get("/health")
 async def health_check():
     return {"status": "healthy"}
 
-@api.post("/translate")
+
+@app.post("/translate")
 async def translate_endpoint(sentences: List[str], target_lang: str):
     try:
         result = translate_text(sentences=sentences, target_lang=target_lang)
@@ -90,73 +86,75 @@ async def translate_endpoint(sentences: List[str], target_lang: str):
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
-# Streamlit interface
-def main():
-    st.title("Indic Language Translator")
-    
-    # Input text
-    text_input = st.text_area("Enter text to translate:", "Hello, how are you?")
-    
-    # Language selection
-    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"
-    }
-    
-    target_lang = st.selectbox(
-        "Select target language:",
-        options=list(target_languages.keys())
-    )
-    
-    if st.button("Translate"):
-        try:
-            result = translate_text(
-                sentences=[text_input],
-                target_lang=target_languages[target_lang]
-            )
-            st.success("Translation:")
-            st.write(result["translations"][0])
-        except Exception as e:
-            st.error(f"Translation failed: {str(e)}")
-
-    # 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"
-    }
-    ```
-    """)
-    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()
+
+# # Streamlit interface
+# def main():
+#     st.title("Indic Language Translator")
+
+#     # Input text
+#     text_input = st.text_area("Enter text to translate:", "Hello, how are you?")
+
+#     # Language selection
+#     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",
+#     }
+
+#     target_lang = st.selectbox(
+#         "Select target language:", options=list(target_languages.keys())
+#     )
+
+#     if st.button("Translate"):
+#         try:
+#             result = translate_text(
+#                 sentences=[text_input], target_lang=target_languages[target_lang]
+#             )
+#             st.success("Translation:")
+#             st.write(result["translations"][0])
+#         except Exception as e:
+#             st.error(f"Translation failed: {str(e)}")
+
+#     # Add API documentation
+#     st.markdown("---")
+#     st.header("API Documentation")
+#     st.markdown(
+#         """
+#     To use the translation API, send POST requests to:
+#     ```
+#     https://darshankr-trans-en-indic.hf.space/translate
+#     ```
+#     Request body format:
+#     ```json
+#     {
+#         "sentences": ["Your text here"],
+#         "target_lang": "hin_Deva"
+#     }
+#     ```
+#     """
+#     )
+#     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()