Update app.py

app.py

@@ -1,40 +1,41 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import torch
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import os
-import sys
-
-sys.path.append(os.path.abspath("libs/IndicTransToolkit"))
-from IndicTransToolkit.processor import IndicProcessor
-
-app = FastAPI(title="IndicTrans Translator API")
-
-ip = IndicProcessor(inference=True)
-tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
-model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
-
-class TranslationRequest(BaseModel):
-    text: str
-    target_lang: str  
-
-@app.post("/translate")
-def translate_text(req: TranslationRequest):
-    if not req.text.strip():
-        raise HTTPException(status_code=400, detail="Input text is empty.")
-
-    try:
-        batch = ip.preprocess_batch([req.text], src_lang="eng_Latn", tgt_lang=req.target_lang)
-        batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt")
-
-        with torch.inference_mode():
-            outputs = model.generate(**batch, num_beams=5, max_length=256)
-
-        with tokenizer.as_target_tokenizer():
-            decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-
-        translated = ip.postprocess_batch(decoded, lang=req.target_lang)[0]
-        return {"translation": translated}
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+import streamlit as st
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import os
+import sys
+
+# Add local IndicTransToolkit path
+sys.path.append(os.path.abspath("libs/IndicTransToolkit"))
+from IndicTransToolkit.processor import IndicProcessor
+
+# Load processor and model
+st.title("IndicTrans Translator")
+st.write("Translate English text into Indian languages using IndicTrans2.")
+
+ip = IndicProcessor(inference=True)
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
+model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
+
+text = st.text_area("Enter text in English:", height=150)
+target_lang = st.selectbox("Select Target Language", [
+    "hin_Deva", "ben_Beng", "pan_Guru", "guj_Gujr", "tam_Taml", "tel_Telu", "mal_Mlym", "mar_Deva", "kan_Knda", "asm_Beng"
+])
+
+if st.button("Translate"):
+    if not text.strip():
+        st.warning("Please enter some text.")
+    else:
+        try:
+            batch = ip.preprocess_batch([text], src_lang="eng_Latn", tgt_lang=target_lang)
+            batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt")
+
+            with torch.inference_mode():
+                outputs = model.generate(**batch, num_beams=5, max_length=256)
+
+            with tokenizer.as_target_tokenizer():
+                decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+            translated = ip.postprocess_batch(decoded, lang=target_lang)[0]
+            st.success(f"Translation: {translated}")
+        except Exception as e:
+            st.error(f"Error: {e}")