Update app.py

app.py

@@ -1,75 +1,75 @@
-import os
-import asyncio
-import torch
-import streamlit as st
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
-from googletrans import Translator
-import langdetect
-
-# ✅ Disable file watching for PyTorch compatibility with Streamlit
-os.environ["STREAMLIT_WATCH_FILE_SYSTEM"] = "false"
-
-# ✅ Fix for asyncio event loop crash on Windows
-try:
-    asyncio.get_running_loop()
-except RuntimeError:
-    asyncio.set_event_loop(asyncio.new_event_loop())
-
-# ✅ Load Fine-Tuned Chhattisgarhi Translation Model
-model_path = "chhattisgarhi_translator"
-model = MBartForConditionalGeneration.from_pretrained(model_path)
-tokenizer = MBart50TokenizerFast.from_pretrained(model_path, src_lang="hi_IN", tgt_lang="hne_IN")
-translator = Translator()
-
-# ✅ Detect Language
-def detect_language(text):
-    try:
-        return langdetect.detect(text)
-    except:
-        return "unknown"
-
-# ✅ Translate English → Hindi
-def translate_english_to_hindi(text):
-    translated = translator.translate(text, src="en", dest="hi")
-    return translated.text
-
-# ✅ Translate Hindi → Chhattisgarhi
-def translate_hindi_to_chhattisgarhi(text):
-    sentences = text.split("।")  # Sentence splitting
-    translated_sentences = []
-
-    for sentence in sentences:
-        sentence = sentence.strip()
-        if sentence:
-            inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding="longest", max_length=256)
-            with torch.no_grad():
-                translated_ids = model.generate(**inputs, max_length=256, num_beams=5, early_stopping=True)
-            translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
-            translated_sentences.append(translated_text)
-
-    return " । ".join(translated_sentences)
-
-# ✅ Streamlit UI
-st.title("English/Hindi to Chhattisgarhi Translator 🗣️")
-st.write("Enter an English or Hindi sentence and get its translation in Chhattisgarhi.")
-
-user_input = st.text_area("Enter text:")
-
-if st.button("Translate"):
-    if user_input.strip():
-        lang = detect_language(user_input)
-
-        if lang == "en":
-            hindi_text = translate_english_to_hindi(user_input)
-            chhattisgarhi_text = translate_hindi_to_chhattisgarhi(hindi_text)
-            st.success(f"**Hindi Translation**:\n{hindi_text}")
-            st.success(f"**Chhattisgarhi Translation**:\n{chhattisgarhi_text}")
-
-        elif lang == "hi":
-            chhattisgarhi_text = translate_hindi_to_chhattisgarhi(user_input)
-            st.success(f"**Chhattisgarhi Translation**:\n{chhattisgarhi_text}")
-
-        else:
-            st.error("❌ Unable to detect language. Please enter text in English or Hindi.")
-    else:
-        st.warning("⚠ Please enter some text before translating.")
+import os
+import asyncio
+import torch
+import streamlit as st
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from googletrans import Translator
+import langdetect
+
+# ✅ Disable file watching for PyTorch compatibility with Streamlit
+os.environ["STREAMLIT_WATCH_FILE_SYSTEM"] = "false"
+
+# ✅ Fix for asyncio event loop crash on Windows
+try:
+    asyncio.get_running_loop()
+except RuntimeError:
+    asyncio.set_event_loop(asyncio.new_event_loop())
+
+# ✅ Load Fine-Tuned Chhattisgarhi Translation Model
+model_path = "app90/ChhattishgarhiAI_Model"
+model = MBartForConditionalGeneration.from_pretrained(model_path)
+tokenizer = MBart50TokenizerFast.from_pretrained(model_path, src_lang="hi_IN", tgt_lang="hne_IN")
+translator = Translator()
+
+# ✅ Detect Language
+def detect_language(text):
+    try:
+        return langdetect.detect(text)
+    except:
+        return "unknown"
+
+# ✅ Translate English → Hindi
+def translate_english_to_hindi(text):
+    translated = translator.translate(text, src="en", dest="hi")
+    return translated.text
+
+# ✅ Translate Hindi → Chhattisgarhi
+def translate_hindi_to_chhattisgarhi(text):
+    sentences = text.split("।")  # Sentence splitting
+    translated_sentences = []
+
+    for sentence in sentences:
+        sentence = sentence.strip()
+        if sentence:
+            inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding="longest", max_length=256)
+            with torch.no_grad():
+                translated_ids = model.generate(**inputs, max_length=256, num_beams=5, early_stopping=True)
+            translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
+            translated_sentences.append(translated_text)
+
+    return " । ".join(translated_sentences)
+
+# ✅ Streamlit UI
+st.title("English/Hindi to Chhattisgarhi Translator 🗣️")
+st.write("Enter an English or Hindi sentence and get its translation in Chhattisgarhi.")
+
+user_input = st.text_area("Enter text:")
+
+if st.button("Translate"):
+    if user_input.strip():
+        lang = detect_language(user_input)
+
+        if lang == "en":
+            hindi_text = translate_english_to_hindi(user_input)
+            chhattisgarhi_text = translate_hindi_to_chhattisgarhi(hindi_text)
+            st.success(f"**Hindi Translation**:\n{hindi_text}")
+            st.success(f"**Chhattisgarhi Translation**:\n{chhattisgarhi_text}")
+
+        elif lang == "hi":
+            chhattisgarhi_text = translate_hindi_to_chhattisgarhi(user_input)
+            st.success(f"**Chhattisgarhi Translation**:\n{chhattisgarhi_text}")
+
+        else:
+            st.error("❌ Unable to detect language. Please enter text in English or Hindi.")
+    else:
+        st.warning("⚠ Please enter some text before translating.")