Update main.py

main.py

@@ -1,61 +1,62 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from transformers import pipeline
-from peft import PeftModel
-
-st.set_page_config(page_title="Kurdish Translator", layout="centered")
-st.title("Kurdish ↔ English Translator (NLLB + LoRA)")
-
-# Model paths
-BASE_MODEL = "facebook/nllb-200-distilled-600M"
-ADAPTER_PATH = "./nllb_kurdish_lora_adapter"
-
-
-@st.cache_resource
-def load_translation_pipeline(direction):
-    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-
-    # Load base model
-    base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL)
-
-    # Load LoRA adapter
-    model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)
-
-    if direction == "English → Kurdish":
-        src = "eng_Latn"
-        tgt = "ckb_Arab"
-    else:  # Kurdish → English
-        src = "ckb_Arab"
-        tgt = "eng_Latn"
-
-    # Build translation pipeline
-    translator = pipeline(
-        "translation",
-        model=model,
-        tokenizer=tokenizer,
-        src_lang=src,
-        tgt_lang=tgt,
-        max_length=256
-    )
-
-    return translator
-
-
-# UI: Translation direction
-direction = st.selectbox(
-    "Select Translation Direction",
-    ["English → Kurdish", "Kurdish → English"]
-)
-
-# Text input box
-text = st.text_area("Enter text below:", height=180)
-
-# Translate Button
-if st.button("Translate"):
-    if text.strip():
-        translator = load_translation_pipeline(direction)
-        output = translator(text)[0]["translation_text"]
-        st.subheader("Translation:")
-        st.success(output)
-    else:
-        st.warning("Please enter some text to translate.")
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from peft import PeftModel
+
+st.set_page_config(page_title="Kurdish Translator", layout="centered")
+st.title("Kurdish ↔ English Translator (NLLB + LoRA)")
+
+# HuggingFace model repos
+BASE_MODEL = "facebook/nllb-200-distilled-600M"
+LORA_REPO = "junaid17/nllb-kurdish-lora"   # <-- use HF model repo, NOT local folder
+
+
+@st.cache_resource
+def load_translation_pipeline(direction):
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+    # Load base NLLB model
+    base_model = AutoModelForSeq2SeqLM.from_pretrained(BASE_MODEL)
+
+    # Load LoRA adapter from HuggingFace Hub
+    model = PeftModel.from_pretrained(base_model, LORA_REPO)
+
+    # Language codes for NLLB
+    if direction == "English → Kurdish":
+        src = "eng_Latn"
+        tgt = "ckb_Arab"
+    else:
+        src = "ckb_Arab"
+        tgt = "eng_Latn"
+
+    # Build translation pipeline
+    translator = pipeline(
+        "translation",
+        model=model,
+        tokenizer=tokenizer,
+        src_lang=src,
+        tgt_lang=tgt,
+        max_length=256,
+    )
+
+    return translator
+
+
+# UI
+direction = st.selectbox(
+    "Select Translation Direction",
+    ["English → Kurdish", "Kurdish → English"]
+)
+
+text = st.text_area("Enter your text:", height=180)
+
+if st.button("Translate"):
+    if not text.strip():
+        st.warning("Please enter some text.")
+    else:
+        with st.spinner("Translating..."):
+            translator = load_translation_pipeline(direction)
+            result = translator(text)[0]["translation_text"]
+
+        st.subheader("Translation:")
+        st.success(result)