Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import chromadb
 from sentence_transformers import SentenceTransformer
-import pandas as pd # Make sure pandas is imported
 
 # --- 1. Load Model (No changes here) ---
 print("Loading sentence-transformer model...")
@@ -15,23 +14,23 @@ try:
     collection = client.create_collection("my_documents")
     print("ChromaDB collection created.")
     
-    # --- THIS IS THE UPDATED SECTION ---
-    # Load your data from the CSV file.
-    # IMPORTANT: Change 'text' to the actual name of the column in your CSV that contains the text data.
+    # --- THIS IS THE UPDATED SECTION FOR READING .TXT ---
     try:
-        print("Loading data from my_data.csv...")
-        df = pd.read_csv('my_data.csv')
-        # Ensure you change 'text' to your column's name if it's different
-        documents = df['text'].tolist() 
-        print(f"Successfully loaded {len(documents)} documents.")
-    except FileNotFoundError:
-        print("Error: my_data.csv not found. Please upload the file to your Space.")
-        # Create a fallback document to avoid a crash
-        documents = ["Error: my_data.csv not found. Please make sure the file is uploaded to the Hugging Face Space."]
-    except KeyError:
-        print("Error: The CSV must have a column named 'text'. Please rename your column or update the code.")
-        documents = ["Error: Could not find a 'text' column in the CSV file. Please check your data."]
+        print("Loading data from my_data.txt...")
+        with open('my_data.txt', 'r', encoding='utf-8') as f:
+            # Each line in the .txt file becomes a separate document.
+            # It also removes any empty lines.
+            documents = [line.strip() for line in f if line.strip()]
+        
+        if not documents:
+            print("Warning: my_data.txt is empty or contains only whitespace.")
+            documents = ["Error: The data file 'my_data.txt' appears to be empty."]
+        else:
+            print(f"Successfully loaded {len(documents)} lines from my_data.txt.")
 
+    except FileNotFoundError:
+        print("Error: my_data.txt not found. Please upload the file to your Space.")
+        documents = ["Error: my_data.txt not found. Please make sure the file is uploaded."]
     # --- END OF UPDATED SECTION ---
     
     embeddings = model.encode(documents)