Update app.py

app.py

@@ -50,65 +50,6 @@ if "debug" not in st.session_state:
 
 st.title("TAPAS Table Question Answering with Weaviate")
 
-# Get existing classes from Weaviate
-existing_classes = [cls["class"] for cls in client.schema.get()["classes"]]
-class_options = existing_classes + ["New Class"]
-selected_class = st.selectbox("Select a class or create a new one:", class_options, key="class_selector")
-
-if selected_class == "New Class":
-    class_name = st.text_input("Enter the new class name:")
-    class_description = st.text_input("Enter a description for the class:")
-else:
-    class_name = selected_class
-    class_description = ""  # We can fetch the description from Weaviate if needed
-
-# Upload CSV data
-csv_file = st.file_uploader("Upload a CSV file", type=["csv"], key="csv_uploader")
-
-# Display the schema if an existing class is selected
-class_schema = None  # Initialize class_schema to None
-if selected_class != "New Class":
-    st.write(f"Schema for {selected_class}:")
-    class_schema = get_class_schema(client, selected_class)
-    if class_schema:
-        properties = class_schema["properties"]
-        schema_df = pd.DataFrame(properties)
-        st.table(schema_df[["name", "dataType"]])  # Display only the name and dataType columns
-
-# Before ingesting data into Weaviate, check if CSV columns match the class schema
-if csv_file is not None:
-    data = csv_file.read().decode("utf-8")
-    dataframe = pd.read_csv(StringIO(data))
-
-    # Log CSV upload information
-    log_debug_info(f"CSV uploaded with shape: {dataframe.shape}")
-    
-    # Display the uploaded CSV data
-    st.write("Uploaded CSV Data:")
-    st.write(dataframe)
-
-    # Check if columns match
-    if class_schema:  # Ensure class_schema is not None
-        schema_columns = [prop["name"] for prop in class_schema["properties"]]
-        if set(dataframe.columns) != set(schema_columns):
-            st.error("The columns in the uploaded CSV do not match the schema of the selected class. Please check and upload the correct CSV or create a new class.")
-        else:
-            # Ingest data into Weaviate
-            ingest_data_to_weaviate(client, dataframe, class_name, class_description)
-
-    # Input for questions
-    questions = st.text_area("Enter your questions (one per line)")
-    questions = questions.split("\n")  # split questions by line
-    questions = [q for q in questions if q]  # remove empty strings
-
-    if st.button("Submit"):
-        if data and questions:
-            answers = summarize_map_reduce(tokenizer, model, data, questions)
-            st.write("Answers:")
-            for q, a in zip(questions, answers):
-                st.write(f"Question: {q}")
-                st.write(f"Answer: {a}")
-
 # Display debugging information
 if st.checkbox("Show Debugging Information"):
     st.write("Debugging Logs:")