Update app.py

app.py

@@ -23,13 +23,13 @@ class StreamlitCallbackHandler(logging.Handler):
         st.write(log_entry)
         
 # Initialize TAPAS model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
-model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
+#tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
+#model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
 
 # Initialize Weaviate client for the embedded instance
-client = weaviate.Client(
-  embedded_options=EmbeddedOptions()
-)
+#client = weaviate.Client(
+#  embedded_options=EmbeddedOptions()
+#)
 
 # Global list to store debugging information
 DEBUG_LOGS = []
@@ -48,65 +48,65 @@ def log_debug_info(message):
 
 
 # Function to check if a class already exists in Weaviate
-def class_exists(class_name):
-    try:
-        client.schema.get_class(class_name)
-        return True
-    except:
-        return False
-
-def map_dtype_to_weaviate(dtype):
-    """
-    Map pandas data types to Weaviate data types.
-    """
-    if "int" in str(dtype):
-        return "int"
-    elif "float" in str(dtype):
-        return "number"
-    elif "bool" in str(dtype):
-        return "boolean"
-    else:
-        return "string"
-
-def ingest_data_to_weaviate(dataframe, class_name, class_description):
-    # Create class schema
-    class_schema = {
-        "class": class_name,
-        "description": class_description,
-        "properties": []  # Start with an empty properties list
-    }
-    
-    # Try to create the class without properties first
-    try:
-        client.schema.create({"classes": [class_schema]})
-    except weaviate.exceptions.SchemaValidationException:
-        # Class might already exist, so we can continue
-        pass
-
-    # Now, let's add properties to the class
-    for column_name, data_type in zip(dataframe.columns, dataframe.dtypes):
-        property_schema = {
-            "name": column_name,
-            "description": f"Property for {column_name}",
-            "dataType": [map_dtype_to_weaviate(data_type)]
-        }
-        try:
-            client.schema.property.create(class_name, property_schema)
-        except weaviate.exceptions.SchemaValidationException:
-            # Property might already exist, so we can continue
-            pass
-
-    # Ingest data
-    for index, row in dataframe.iterrows():
-        obj = {
-            "class": class_name,
-            "id": str(index),
-            "properties": row.to_dict()
-        }
-        client.data_object.create(obj)
+#def class_exists(class_name):
+#    try:
+#        client.schema.get_class(class_name)
+#        return True
+#    except:
+#        return False
+
+#def map_dtype_to_weaviate(dtype):
+##    """
+ #   Map pandas data types to Weaviate data types.
+ #   """
+ #   if "int" in str(dtype):
+ #       return "int"
+ #   elif "float" in str(dtype):
+ #       return "number"
+ #   elif "bool" in str(dtype):
+ #       return "boolean"
+ #   else:
+ #       return "string"
+
+# def ingest_data_to_weaviate(dataframe, class_name, class_description):
+#    # Create class schema
+#    class_schema = {
+#        "class": class_name,
+#        "description": class_description,
+#        "properties": []  # Start with an empty properties list
+#    }
+#    
+#    # Try to create the class without properties first
+ #   try:
+#        client.schema.create({"classes": [class_schema]})
+#    except weaviate.exceptions.SchemaValidationException:
+#        # Class might already exist, so we can continue
+#        pass#
+
+#    # Now, let's add properties to the class
+#    for column_name, data_type in zip(dataframe.columns, dataframe.dtypes):
+#        property_schema = {
+#            "name": column_name,
+#            "description": f"Property for {column_name}",
+#            "dataType": [map_dtype_to_weaviate(data_type)]
+#        }
+#        try:
+#            client.schema.property.create(class_name, property_schema)
+#        except weaviate.exceptions.SchemaValidationException:
+#            # Property might already exist, so we can continue
+#            pass
+#
+#    # Ingest data
+#    for index, row in dataframe.iterrows():
+#        obj = {
+#            "class": class_name,
+#            "id": str(index),
+#            "properties": row.to_dict()
+#        }
+#        client.data_object.create(obj)
 
     # Log data ingestion
-    log_debug_info(f"Data ingested into Weaviate for class: {class_name}")
+#    log_debug_info(f"Data ingested into Weaviate for class: {class_name}")
 
 def query_weaviate(question):
     # This is a basic example; adapt the query based on the question