fix(load): path fixed

pandasai/__init__.py

@@ -86,6 +86,10 @@ def load(dataset_path: str, virtualized=False) -> DataFrame:
     Returns:
         DataFrame: A new PandasAI DataFrame instance with loaded data.
     """
+    path_parts = dataset_path.split("/")
+    if len(path_parts) != 2:
+        raise ValueError("Path must be in format 'organization/dataset'")
+
     global _dataset_loader
     dataset_full_path = os.path.join(find_project_root(), "datasets", dataset_path)
     if not os.path.exists(dataset_full_path):

pandasai/chat/code_execution/environment.py

@@ -6,7 +6,7 @@
 import importlib
 import sys
 import warnings
-from typing import List
+from typing import List, Union
 
 from pandas.util.version import Version
 
@@ -92,7 +92,7 @@ def import_dependency(
     name: str,
     extra: str = "",
     errors: str = "raise",
-    min_version: str | None = None,
+    min_version: Union[str, None] = None,
 ):
     """
     Import an optional dependency.

pandasai/data_loader/loader.py

@@ -47,6 +47,13 @@ def load(self, dataset_path: str, virtualized=False) -> DataFrame:
             )
         else:
             # Initialize new dataset loader for virtualization
+            source_type = self.schema["source"]["type"]
+
+            if source_type in ["csv", "parquet"]:
+                raise ValueError(
+                    "Virtualization is not supported for CSV and Parquet files."
+                )
+
             data_loader = self.copy()
             table_name = self.schema["source"].get("table", None) or self.schema["name"]
             table_description = self.schema.get("description", None)
@@ -58,10 +65,11 @@ def load(self, dataset_path: str, virtualized=False) -> DataFrame:
                 path=dataset_path,
             )
 
+    def _get_abs_dataset_path(self):
+        return os.path.join(find_project_root(), "datasets", self.dataset_path)
+
     def _load_schema(self):
-        schema_path = os.path.join(
-            find_project_root(), "datasets", self.dataset_path, "schema.yaml"
-        )
+        schema_path = os.path.join(self._get_abs_dataset_path(), "schema.yaml")
         if not os.path.exists(schema_path):
             raise FileNotFoundError(f"Schema file not found: {schema_path}")
 
@@ -79,13 +87,13 @@ def _validate_source_type(self):
     def _get_cache_file_path(self) -> str:
         if "path" in self.schema["destination"]:
             return os.path.join(
-                "datasets", self.dataset_path, self.schema["destination"]["path"]
+                self._get_abs_dataset_path(), self.schema["destination"]["path"]
             )
 
         file_extension = (
             "parquet" if self.schema["destination"]["format"] == "parquet" else "csv"
         )
-        return os.path.join("datasets", self.dataset_path, f"data.{file_extension}")
+        return os.path.join(self._get_abs_dataset_path(), f"data.{file_extension}")
 
     def _is_cache_valid(self, cache_file: str) -> bool:
         if not os.path.exists(cache_file):
@@ -154,10 +162,11 @@ def _read_csv_or_parquet(self, file_path: str, format: str) -> DataFrame:
     def _load_from_source(self) -> pd.DataFrame:
         source_type = self.schema["source"]["type"]
         if source_type in ["csv", "parquet"]:
-            filpath = os.path.join(
-                "datasets", self.dataset_path, self.schema["source"]["path"]
+            filepath = os.path.join(
+                self._get_abs_dataset_path(),
+                self.schema["source"]["path"],
             )
-            return self._read_csv_or_parquet(filpath, source_type)
+            return self._read_csv_or_parquet(filepath, source_type)
 
         query_builder = QueryBuilder(self.schema)
         query = query_builder.build_query()

pandasai/data_loader/query_builder.py

@@ -21,6 +21,16 @@ def _get_columns(self) -> str:
         else:
             return "*"
 
+    def _get_table_name(self):
+        table_name = self.schema["source"].get("table", None) or self.schema["name"]
+
+        if not table_name:
+            raise ValueError("Table name not found in schema!")
+
+        table_name = table_name.lower()
+
+        return table_name
+
     def _add_order_by(self) -> str:
         if "order_by" not in self.schema:
             return ""
@@ -40,16 +50,14 @@ def get_head_query(self, n=5):
         source = self.schema.get("source", {})
         source_type = source.get("type")
 
-        table_name = self.schema["source"]["table"]
+        table_name = self._get_table_name()
 
         columns = self._get_columns()
 
-        order_by = "RAND()"
-        if source_type in {"sqlite", "postgres"}:
-            order_by = "RANDOM()"
+        order_by = "RANDOM()" if source_type in {"sqlite", "postgres"} else "RAND()"
 
         return f"SELECT {columns} FROM {table_name} ORDER BY {order_by} LIMIT {n}"
 
     def get_row_count(self):
-        table_name = self.schema["source"]["table"]
+        table_name = self._get_table_name()
         return f"SELECT COUNT(*) FROM {table_name}"