Update app.py

app.py

@@ -2,34 +2,45 @@ import gradio as gr
 import pandas as pd
 from sentence_transformers import SentenceTransformer, util
 
-# Load data
+# Load files
 df = pd.read_excel("IslamWeb_output.xlsx")
 df2 = pd.read_excel("JordanFatwas_all.xlsx")
+
+# Validate
+for d, name in [(df, "IslamWeb"), (df2, "JordanFatwas")]:
+    if not {"question", "link"}.issubset(d.columns):
+        raise ValueError(f"❌ Missing required columns in {name}")
+
+# Load model + encode
 model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
-embeddings = model.encode(df["question"].tolist(), convert_to_tensor=True)
-embeddings2 = model.encode(df2["question"].tolist(), convert_to_tensor=True)
+embeddings = model.encode(df["question"].fillna('').tolist(), convert_to_tensor=True)
+embeddings2 = model.encode(df2["question"].fillna('').tolist(), convert_to_tensor=True)
 
+# Define function
 def search_fatwa(query):
     query_embedding = model.encode(query, convert_to_tensor=True)
+
     scores = util.pytorch_cos_sim(query_embedding, embeddings)[0]
     top_idx = int(scores.argmax())
-    
+
     scores2 = util.pytorch_cos_sim(query_embedding, embeddings2)[0]
     top_idx2 = int(scores2.argmax())
+
     return {
         "question1": df.iloc[top_idx]["question"],
         "link1": df.iloc[top_idx]["link"],
         "question2": df2.iloc[top_idx2]["question"],
         "link2": df2.iloc[top_idx2]["link"],
     }
-print("AAAAA")
+
+# Interface
 iface = gr.Interface(
     fn=search_fatwa,
     inputs="text",
     outputs="json",
     allow_flagging="never",
-    title="Fatwa Search",
-    description="Ask a question and receive a relevant fatwa with a verified link"
+    title="Fatwa Search (Dual Source)",
+    description="Get the most relevant fatwas from both datasets"
 )
 
 iface.launch()