Update app.py

app.py

@@ -110,7 +110,6 @@
 # iface.launch()
 
 
-
 import torch
 import pandas as pd
 from sentence_transformers import SentenceTransformer, util
@@ -122,19 +121,14 @@ df2 = pd.read_csv("cleaned2.csv")
 embeddings = torch.load("embeddings1.pt")
 embeddings2 = torch.load("embeddings2.pt")
 
-def search_fatwa(query):
-    # Handle both string and list inputs
-    if isinstance(query, list):
-        query = query[0] if query else ""
-    
-    if not query or query.strip() == "":
+def predict(text):
+    if not text or text.strip() == "":
         return "No query provided"
     
-    query_embedding = model.encode(query, convert_to_tensor=True)
+    query_embedding = model.encode(text, convert_to_tensor=True)
     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].argmax())
     
-    # Return formatted text (like your working first app)
     result = f"""Question 1: {df.iloc[top_idx]["question"]}
 Link 1: {df.iloc[top_idx]["link"]}
 
@@ -143,13 +137,12 @@ Link 2: {df2.iloc[top_idx2]["link"]}"""
     
     return result
 
-# Use the same structure as your working first app
+# Match the EXACT structure of your working translation app
+title = "Search CSV"
 iface = gr.Interface(
-    fn=search_fatwa, 
+    fn=predict,  # Changed from search_fatwa to predict 
     inputs=[gr.Textbox(label="text", lines=3)],
-    outputs='text',  # Changed to 'text' like your working app
-    title="Search CSV"
+    outputs='text',
+    title=title,
 )
-
-# Enable API access for curl requests
-iface.launch(share=False, show_api=True)
+iface.launch()