feat(Dataframe): pull method to fetch dataset from remote server

pandasai/__init__.py

@@ -3,9 +3,16 @@
 PandasAI is a wrapper around a LLM to make dataframes conversational
 """
 
+from io import BytesIO
+import os
 from typing import List
+from zipfile import ZipFile
 
 import pandas as pd
+
+from pandasai.exceptions import DatasetNotFound, PandasAIApiKeyError
+from pandasai.helpers.path import find_project_root
+from pandasai.helpers.request import get_pandaai_session
 from .agent import Agent
 from .helpers.cache import Cache
 from .dataframe.base import DataFrame
@@ -74,6 +81,28 @@ def load(dataset_path: str, virtualized=False) -> DataFrame:
         DataFrame: A new PandasAI DataFrame instance with loaded data.
     """
     global _dataset_loader
+    dataset_full_path = os.path.join(find_project_root(), "datasets", dataset_path)
+    if not os.path.exists(dataset_full_path):
+        api_key = os.environ.get("PANDAAI_API_KEY", None)
+        api_url = os.environ.get("PANDAAI_API_URL", None)
+        if not api_url or not api_key:
+            raise PandasAIApiKeyError(
+                "Set PANDAAI_API_URL and PANDAAI_API_KEY in environment to pull dataset from the remote server"
+            )
+
+        request_session = get_pandaai_session()
+
+        headers = {"accept": "application/json", "x-authorization": f"Bearer {api_key}"}
+
+        file_data = request_session.get(
+            "/datasets/pull", headers=headers, params={"path": dataset_path}
+        )
+        if file_data.status_code != 200:
+            raise DatasetNotFound("Dataset not found!")
+
+        with ZipFile(BytesIO(file_data.content)) as zip_file:
+            zip_file.extractall(dataset_full_path)
+
     return _dataset_loader.load(dataset_path, virtualized)
 
 

pandasai/data_loader/loader.py

@@ -35,16 +35,25 @@ def load(self, dataset_path: str, virtualized=False) -> DataFrame:
             self._cache_data(df, cache_file)
 
             table_name = self.schema["source"]["table"]
+            table_description = self.schema.get("description", None)
 
-            return DataFrame(df, schema=self.schema, name=table_name, path=dataset_path)
+            return DataFrame(
+                df,
+                schema=self.schema,
+                name=table_name,
+                description=table_description,
+                path=dataset_path,
+            )
         else:
             # Initialize new dataset loader for virtualization
             data_loader = self.copy()
             table_name = self.schema["source"]["table"]
+            table_description = self.schema.get("description", None)
             return VirtualDataFrame(
                 schema=self.schema,
                 data_loader=data_loader,
                 name=table_name,
+                description=table_description,
                 path=dataset_path,
             )
 
@@ -88,10 +97,24 @@ def _is_cache_valid(self, cache_file: str) -> bool:
 
     def _read_cache(self, cache_file: str) -> DataFrame:
         cache_format = self.schema["destination"]["format"]
+        table_name = self.schema["source"]["table"]
+        table_description = self.schema.get("description", None)
         if cache_format == "parquet":
-            return DataFrame(pd.read_parquet(cache_file))
+            return DataFrame(
+                pd.read_parquet(cache_file),
+                schema=self.schema,
+                path=self.dataset_path,
+                name=table_name,
+                description=table_description,
+            )
         elif cache_format == "csv":
-            return DataFrame(pd.read_csv(cache_file))
+            return DataFrame(
+                pd.read_csv(cache_file),
+                schema=self.schema,
+                path=self.dataset_path,
+                name=table_name,
+                description=table_description,
+            )
         else:
             raise ValueError(f"Unsupported cache format: {cache_format}")
 

pandasai/dataframe/base.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
+from io import BytesIO
 import os
 import re
+from zipfile import ZipFile
 import pandas as pd
 from typing import TYPE_CHECKING, List, Optional, Union, Dict, ClassVar
 
@@ -9,13 +11,13 @@
 
 from pandasai.config import Config
 import hashlib
-from pandasai.exceptions import PandasAIApiKeyError
+from pandasai.exceptions import DatasetNotFound, PandasAIApiKeyError
 from pandasai.helpers.dataframe_serializer import (
     DataframeSerializer,
     DataframeSerializerType,
 )
 from pandasai.helpers.path import find_project_root
-from pandasai.helpers.request import Session
+from pandasai.helpers.request import get_pandaai_session
 
 
 if TYPE_CHECKING:
@@ -220,14 +222,9 @@ def save(
         print(f"Dataset saved successfully to path: {dataset_directory}")
 
     def push(self):
-        api_url = os.environ.get("PANDAAI_API_URL", None)
         api_key = os.environ.get("PANDAAI_API_KEY", None)
-        if not api_url or not api_key:
-            raise PandasAIApiKeyError(
-                "Set PANDAAI_API_URL and PANDAAI_API_KEY in environment to push dataset to the remote server"
-            )
 
-        request_session = Session(endpoint_url=api_url, api_key=api_key)
+        request_session = get_pandaai_session()
 
         params = {
             "path": self.path,
@@ -255,3 +252,47 @@ def push(self):
                 params=params,
                 headers=headers,
             )
+
+    def pull(self):
+        api_key = os.environ.get("PANDAAI_API_KEY", None)
+
+        if not api_key:
+            raise PandasAIApiKeyError(
+                "Set PANDAAI_API_URL and PANDAAI_API_KEY in environment to pull dataset to the remote server"
+            )
+
+        request_session = get_pandaai_session()
+
+        headers = {"accept": "application/json", "x-authorization": f"Bearer {api_key}"}
+
+        file_data = request_session.get(
+            "/datasets/pull", headers=headers, params={"path": self.path}
+        )
+        if file_data.status_code != 200:
+            raise DatasetNotFound("Remote dataset not found to pull!")
+
+        with ZipFile(BytesIO(file_data.content)) as zip_file:
+            for file_name in zip_file.namelist():
+                target_path = os.path.join(
+                    find_project_root(), "datasets", self.path, file_name
+                )
+
+                # Check if the file already exists
+                if os.path.exists(target_path):
+                    print(f"Replacing existing file: {target_path}")
+
+                # Ensure target directory exists
+                os.makedirs(os.path.dirname(target_path), exist_ok=True)
+
+                # Extract the file
+                with open(target_path, "wb") as f:
+                    f.write(zip_file.read(file_name))
+
+        # reloads the Dataframe
+        from pandasai import DatasetLoader
+
+        dataset_loader = DatasetLoader()
+        df = dataset_loader.load(self.path, virtualized=not isinstance(self, DataFrame))
+        self.__init__(
+            df, schema=df.schema, name=df.name, description=df.description, path=df.path
+        )

pandasai/exceptions.py

@@ -209,8 +209,9 @@ class PandasAIApiKeyError(Exception):
         Exception (Exception): PandasAIApiKeyError
     """
 
-    def __init__(self):
-        message = PANDASBI_SETUP_MESSAGE
+    def __init__(self, message: str = None):
+        if not message:
+            message = PANDASBI_SETUP_MESSAGE
         super().__init__(message)
 
 
@@ -264,3 +265,11 @@ class MaliciousCodeGenerated(Exception):
     Args:
         Exception (Exception): MaliciousCodeGenerated
     """
+
+
+class DatasetNotFound(Exception):
+    """
+    Raise error if dataset not found
+    Args:
+        Exception (Exception): DatasetNotFound
+    """

pandasai/helpers/request.py

@@ -35,7 +35,7 @@ def __init__(
         self._logger = logger or Logger()
 
     def get(self, path=None, **kwargs):
-        return self.make_request("GET", path, **kwargs)["data"]
+        return self.make_request("GET", path, **kwargs)
 
     def post(self, path=None, **kwargs):
         return self.make_request("POST", path, **kwargs)
@@ -79,7 +79,12 @@ def make_request(
                 **kwargs,
             )
 
-            data = response.json()
+            try:
+                data = response.json()
+            except ValueError:
+                if response.status_code == 200:
+                    return response
+
             if response.status_code not in [200, 201]:
                 if "message" in data:
                     raise PandasAIApiCallError(data["message"])
@@ -91,3 +96,14 @@ def make_request(
         except requests.exceptions.RequestException as e:
             self._logger.log(f"Request failed: {traceback.format_exc()}", logging.ERROR)
             raise PandasAIApiCallError(f"Request failed: {e}") from e
+
+
+def get_pandaai_session():
+    api_url = os.environ.get("PANDAAI_API_URL", None)
+    api_key = os.environ.get("PANDAAI_API_KEY", None)
+    if not api_url or not api_key:
+        raise PandasAIApiKeyError(
+            "Set PANDAAI_API_URL and PANDAAI_API_KEY in environment to push/pull dataset to the remote server"
+        )
+
+    return Session(endpoint_url=api_url, api_key=api_key)

pandasai/vectorstores/bamboo_vectorstore.py

@@ -58,7 +58,7 @@ def get_relevant_qa_documents(self, question: str, k: int = None) -> List[dict]:
         try:
             docs = self._session.get(
                 "/training-data/qa/relevant-qa", params={"query": question, "count": k}
-            )
+            )["data"]
             return docs["docs"]
         except Exception:
             self._logger.log("Querying without using training data.", logging.ERROR)
@@ -77,7 +77,7 @@ def get_relevant_docs_documents(
             docs = self._session.get(
                 "/training-docs/docs/relevant-docs",
                 params={"query": question, "count": k},
-            )
+            )["data"]
             return docs["docs"]
         except Exception:
             self._logger.log("Querying without using training docs.", logging.ERROR)