Update app.py

app.py

@@ -4,50 +4,56 @@ import os
 import pandas as pd
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-# Paths
 ZIP_FILE = "xnli-multilingual-nli-dataset.zip"
 EXTRACT_DIR = "extracted_data"
 
 @st.cache_data
-def extract_and_load():
+def extract_and_list_csv_files():
     if not os.path.exists(EXTRACT_DIR):
+        if not os.path.exists(ZIP_FILE):
+            return []
         with zipfile.ZipFile(ZIP_FILE, "r") as zip_ref:
             zip_ref.extractall(EXTRACT_DIR)
-    csv_files = [f for f in os.listdir(EXTRACT_DIR) if f.endswith('.csv')]
-    return csv_files
+    return [f for f in os.listdir(EXTRACT_DIR) if f.endswith('.csv')]
 
-# Load model and tokenizer
 @st.cache_resource
-def load_model():
+def load_nli_model():
     tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
     model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
-    nli_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
-    return nli_pipeline
-
-st.set_page_config(page_title="Multilingual NLI App", layout="centered")
+    return pipeline("text-classification", model=model, tokenizer=tokenizer)
 
+st.set_page_config(page_title="Multilingual NLI Explorer", layout="centered")
 st.title("🌍 Multilingual NLI (Natural Language Inference) Explorer")
 st.markdown("Upload premise & hypothesis pairs or use the dataset to explore entailment, contradiction, or neutrality.")
 
-nli_pipeline = load_model()
+csv_files = extract_and_list_csv_files()
+
+if not csv_files:
+    st.warning("⚠️ No CSV files found. Please make sure `xnli-multilingual-nli-dataset.zip` is uploaded.")
+else:
+    selected_csv = st.selectbox("Choose a language CSV file:", csv_files)
 
-csv_files = extract_and_load()
-selected_csv = st.selectbox("Choose a language CSV file:", csv_files)
+    if selected_csv:
+        file_path = os.path.join(EXTRACT_DIR, selected_csv)
 
-df = pd.read_csv(os.path.join(EXTRACT_DIR, selected_csv)).dropna()
-sample_df = df.sample(5).reset_index(drop=True)
+        try:
+            df = pd.read_csv(file_path).dropna()
+            sample_df = df.sample(min(5, len(df))).reset_index(drop=True)
 
-st.subheader("Sample from Dataset")
-st.dataframe(sample_df[['premise', 'hypothesis', 'label']])
+            st.subheader("📄 Sample from Dataset")
+            st.dataframe(sample_df[['premise', 'hypothesis', 'label']])
 
-st.subheader("🔍 Run Inference")
-index = st.number_input("Select Sample Index", min_value=0, max_value=len(sample_df)-1, value=0, step=1)
-premise = sample_df.loc[index, 'premise']
-hypothesis = sample_df.loc[index, 'hypothesis']
+            st.subheader("🔍 Run Inference")
+            index = st.number_input("Select Sample Index", min_value=0, max_value=len(sample_df)-1, value=0, step=1)
+            premise = sample_df.loc[index, 'premise']
+            hypothesis = sample_df.loc[index, 'hypothesis']
 
-st.markdown(f"**Premise:** {premise}")
-st.markdown(f"**Hypothesis:** {hypothesis}")
+            st.markdown(f"**Premise:** {premise}")
+            st.markdown(f"**Hypothesis:** {hypothesis}")
 
-if st.button("Run NLI Prediction"):
-    result = nli_pipeline(f"{premise} </s> {hypothesis}")
-    st.success(f"**Prediction:** {result[0]['label']} (Score: {result[0]['score']:.2f})")
+            if st.button("Run NLI Prediction"):
+                nli_pipeline = load_nli_model()
+                result = nli_pipeline(f"{premise} </s> {hypothesis}")
+                st.success(f"**Prediction:** {result[0]['label']} (Score: {result[0]['score']:.2f})")
+        except Exception as e:
+            st.error(f"❌ Error reading CSV file: {e}")