Merge pull request #2 from DashyDashOrg/v0.0.5

v0.0.5

README.md

@@ -38,7 +38,7 @@ data = [('John Doe', 25, 50),
         ('Olivia Jackson', 29, 55)]
 df = pd.DataFrame(data, columns=['name', 'age', 'donation'])
 
-conv_df = PandasLLM(data=df, llm_api_key = os.environ.get("OPENAI_API_KEY"))
+conv_df = PandasLLM(data=df, llm_api_key = os.environ.get("OPENAI_API_KEY"), verbose=True)
 result = conv_df.prompt("What is the average donation of people older than 30 who donated more than $50?")
 
 print(f"Result ({type(result)}):\n {result}")

pandas_llm/__init__.py

@@ -1 +1,359 @@
-from pandas_llm import PandasLLM
+import pandas as pd
+import datetime
+import numpy as np
+import openai
+import os
+import re
+import json
+
+# sandbox.py
+from RestrictedPython import compile_restricted
+from RestrictedPython.Guards import safe_builtins,guarded_iter_unpack_sequence
+from  RestrictedPython.Eval import default_guarded_getattr, default_guarded_getitem, default_guarded_getiter
+import pandas as pd
+
+class Sandbox:
+    def __init__(self):
+        self._allowed_imports = {}
+
+    def allow_import(self, module_name):
+        try:
+            module = __import__(module_name)
+            self._allowed_imports[module_name] = module
+        except ImportError:
+            pass
+
+    def execute(self, code, local_vars = {}):
+        allowed_builtins = safe_builtins
+        # Add __builtins__, __import__, and allowed imports to the globals
+        restricted_globals = {"__builtins__": allowed_builtins}
+        restricted_globals.update(self._allowed_imports)
+
+        builtin_mappings = {
+            "__import__": __import__,
+            "_getattr_": default_guarded_getattr,
+            "_getitem_": default_guarded_getitem,
+            "_getiter_": default_guarded_getiter,
+            "_iter_unpack_sequence_": guarded_iter_unpack_sequence,
+            "list": list,
+            "set": set,
+            "pd": pd,
+        }
+
+        series_methods = [
+            "sum", "mean", "any", "argmax", "argmin", "count", "cumsum", "cumprod", "diff",
+            "dropna", "fillna", "head", "idxmax", "idxmin", "last", "max", "min", "notna",
+            "prod", "quantile", "rename", "round", "tail", "to_frame", "to_list", "to_numpy",
+            "to_string","unique",  "sort_index", "sort_values", "aggregate"
+        ]
+
+
+        builtin_mappings.update({method: getattr(pd.Series, method) for method in series_methods})
+
+        restricted_globals["__builtins__"].update(builtin_mappings)
+
+        byte_code = compile_restricted(source=code, filename='<inline>', mode='exec')
+
+        # Execute the restricted code
+        exec(byte_code, restricted_globals, local_vars)
+
+        return local_vars
+
+
+class PandasLLM(pd.DataFrame):
+    """
+    PandasLLM is a subclass of the Pandas DataFrame class. It is designed to provide a
+    wrapper around the OpenAI API. 
+    """
+
+    code_blocks = [r'```python(.*?)```',r'```(.*?)```']
+
+    llm_default_model = "gpt-3.5-turbo"
+    llm_default_temperature = 0.2
+    llm_engine = "openai"
+    llm_default_params = { "model": llm_default_model,
+                            "temperature": llm_default_temperature}
+    llm_api_key = None
+
+    prompt_override = False
+    custom_prompt = ""
+    data_privacy = True
+    path = None
+    verbose = False 
+    code_block = ""
+    force_sandbox = False
+    def __init__(self, 
+                 data, 
+                 llm_engine:str = "openai", llm_params=llm_default_params, 
+                 prompt_override:bool = False,     
+                 custom_prompt:str = "", 
+                 path:str = None,
+                 verbose:bool = False,
+                 data_privacy:bool = True,
+                 llm_api_key:str = None,
+                 force_sandbox:bool = False,
+                 *args, **kwargs):
+        """
+        This is the constructor for the PandasLLM class. It takes in the following arguments:
+        data: The data to be used. It can be a Pandas DataFrame, a list of lists, a list of tuples,
+        a list of dictionaries, a dictionary, a string, or a list.
+        llm_engine: The name of the OpenAI engine to use.
+        llm_params: A dictionary of parameters to be used with the OpenAI API.
+        prompt_override: A boolean that determines whether or not the prompt is overridden.
+        custom_prompt: A string that overrides the prompt.
+        path: The path to the file to be used.
+        verbose: A boolean that determines whether or not the output is verbose.
+        data_privacy: A boolean that determines whether or not the data is private.
+        llm_api_key: The OpenAI API key to be used.
+        force_sandbox: if False and the sandbox fails, it will retry using eval (less safe)
+
+        The constructor also calls the parent class's constructor.
+
+        
+        Args:
+            data (pandas dataframe, mandatory): dataset to query. Defaults to None.
+            llm_engine (str, optional): LLM engine, currently only OpenAI is supported. Defaults to "openai".
+            llm_params (dict, optional): LLM engine parameters. Defaults to model=gpt-3.5-turbo and temperature=0.2".
+            prompt_override (bool, optional): if True, the custom prompt is mandatory and it will became the main prompt. Defaults to False.
+            custom_prompt (str, optional): if prompt_override is False, the custom prompt will be added to the default pandas_llm prompt. Defaults to "".
+            path (str, optional): the path where the files containing debug data will be save. Defaults to None.
+            verbose (bool, optional): if True debugging info will be printed. Defaults to False.
+            data_privacy (bool, optional): if True, the function will not send the data content to OpenAI. Defaults to True.
+            llm_api_key (str, optional): the Open API key. Defaults to None.
+            force_sandbox (bool, optional): if False and the sandbox fails, it will retry using eval (less safe). Defaults to False.
+        """
+
+
+        super().__init__(data, *args, **kwargs)
+
+        self.llm_params = llm_params or {}
+
+        # Set up OpenAI API key from the environment or the config
+        self.llm_api_key = llm_api_key or os.environ.get("OPENAI_API_KEY")
+
+        self.llm_engine = llm_engine
+        self.llm_params = llm_params or {}
+        self.model = self.llm_params.get("model", self.llm_default_model)
+        self.temperature = self.llm_params.get("temperature", self.llm_default_temperature)
+
+        self.prompt_override = prompt_override
+        self.custom_prompt = custom_prompt
+
+        self.data_privacy = data_privacy
+        self.path = path
+        self.verbose = verbose
+        self.force_sandbox = force_sandbox
+
+    def _buildPromptForRole(self):
+        prompt_role = f"""
+I want you to act as a data scientist and Python coder. I want you code for me. 
+I have a dataset of {len(self)} rows and {len(self.columns)} columns.
+Columns and their type are the following:
+        """
+
+        for col in self.columns:
+            col_type = self.dtypes[col]
+            prompt_role += f"{col} ({col_type})\n"
+
+        return prompt_role
+
+    def _buildPromptForProblemSolving(self, request):
+
+        if self.prompt_override:
+            return self.custom_prompt
+
+        columns = ""
+        for col in self.columns:
+            col_type = self.dtypes[col]
+            columns += f"{col} ({col_type})\n"
+
+        prompt_problem = f"""
+Given a DataFrame named 'df' of {len(self)} rows and {len(self.columns)} columns,
+Its columns are the following:
+
+{columns}
+
+I want you to solve the following problem:
+write a Python code snippet that addresses the following request:
+{request}
+
+While crafting the code, please follow these guidelines:
+1. When comparing or searching for strings, use lower case letters, ignore case sensitivity, and apply a "contains" search.
+2. Ensure that the answer is a single line of code without explanations, comments, or additional details. 
+3. If a single line solution is not possible, multiline solutions or functions are acceptable, but the code must end with an assignment to the variable 'result'.
+4. Assign the resulting code to the variable 'result'.
+5. Avoid importing any additional libraries than pandas and numpy.
+
+"""
+        if not self.custom_prompt is None and len(self.custom_prompt) > 0:
+
+            prompt_problem += f"""
+            Also:
+            {self.custom_prompt}
+            """
+
+        return prompt_problem
+
+    def _extractPythonCode(self, text: str, regexp: str) -> str:
+        # Define the regular expression pattern for the Python code block
+        pattern = regexp
+
+        # Search for the pattern in the input text
+        match = re.search(pattern, text, re.DOTALL)
+
+        # If a match is found, return the extracted code (without the markers)
+        if match:
+            return match.group(1).strip()
+
+        # If no match is found, return an empty string
+        return ""
+
+    def _print(self,  *args, **kwargs):
+        if self.verbose:
+            print(*args, **kwargs)
+
+    # def _variable_to_string(self, variable):
+    #     if variable is None: return None
+    #     try:
+
+    #         if isinstance(variable, pd.Series):
+    #             # convert to dataframe
+    #             variable = variable.to_frame()
+
+    #         if isinstance(variable, pd.DataFrame):
+    #             variable = variable.drop_duplicates()
+    #             if len(variable) == 0: return None
+    #             return str(variable)
+
+    #         elif isinstance(variable, np.ndarray):
+    #             if len(variable) == 0: return None
+    #             return  np.array2string(variable)
+    #         else:
+    #             # Convert the variable to a string
+    #             return str(variable)
+    #     except Exception as e:
+    #         return str(variable)
+
+
+    def _save(self,name,value):
+        if self.path is None or self.path == "":
+            return  
+        try:
+            with open(f"{self.path}/{name}", 'w') as file:
+                file.write(value)
+        except Exception as e:
+            self._print(f"error {e}")
+        return
+
+    def _execInSandbox(self, df, generated_code:str):
+
+        # Create a Sandbox instance and allow pandas to be imported
+        sandbox = Sandbox()
+        sandbox.allow_import("pandas")
+        sandbox.allow_import("numpy")
+
+        # Define the initial code to set up the DataFrame
+        initial_code = f"""
+import pandas as pd
+import datetime
+from pandas import Timestamp
+import numpy as np
+
+        """
+
+        # Combine the initial code and the generated code
+        full_code = initial_code + "\n" + generated_code
+
+        self._save("temp/prompt_code.py",full_code)
+        # Execute the combined code in the Sandbox
+        sandbox_result = sandbox.execute(full_code, {"df":df})
+
+        # Get the result from the local_vars dictionary
+        result = sandbox_result.get("result")
+        return result
+
+    def prompt(self, request: str):
+        """
+
+        Args:
+            request (str): prompt containing the request. it must be expressed as a question or a problem to solve
+
+        Returns:
+            Any: contains the result or solution of the problem. Tipically the result data type is a dataframe, a Series or a float
+        """
+
+        # Set up OpenAI API key
+        openai.api_key = self.llm_api_key
+
+        messages=[
+                {"role": "system", 
+                "content": self._buildPromptForRole()},
+                {"role": "user", 
+                "content": self._buildPromptForProblemSolving(request)
+                }
+            ]
+
+        response = None
+        for times in range(0,3):
+            try:
+                response = openai.ChatCompletion.create(
+                model=self.model,
+                temperature=self.temperature,
+                messages = messages
+                )
+                break;
+            except Exception as e:
+                self._print(f"error {e}")
+                continue
+
+        if response is None:
+            return "Please try later"
+
+        self._save("temp/prompt_cmd.json",json.dumps(messages, indent=4))
+
+        generated_code = response.choices[0].message.content
+        if generated_code == "" or generated_code is None:
+            self.code_block = ""
+            return None
+
+        self.code_block = generated_code
+
+        results=[]
+        for regexp in self.code_blocks:
+            cleaned_code = self._extractPythonCode(generated_code,regexp)
+            if cleaned_code == "" or cleaned_code is None:
+                continue
+            results.append(cleaned_code)
+        results.append(generated_code)
+
+        if len(results) == 0:
+            return None
+
+        result = None
+        for cleaned_code in results:
+
+            try:
+                result = self.execInSandbox(self, cleaned_code)
+            except Exception as e:
+                self._print(f"error {e}")
+                if not self.force_sandbox:
+                    try:
+                        expression = re.sub(r"^\s*result\s*=", "", cleaned_code).strip()
+                        result = eval(expression, {'df': self, 'pd': pd, 'np': np, 'datetime': datetime, 'result': result})
+                    except Exception as e:
+                        self._print(f"error {e}")
+                        pass
+
+            if result is not None and str(result) != "":
+                break
+
+        if self.data_privacy == True:
+            # non formatted result
+            return result
+
+        # currently the privacy option is not needed.
+        # in the future, we can choose to send data to LLM if privacy is set to false
+
+        return result
+
+

pandas_llm/example-chatbot.py

@@ -63,7 +63,7 @@ def main():
     Please note that these names, ages, and donations are randomly generated and do not correspond to real individuals or their donations.
     
     You can ask questions like:
-    - show me the name of donors
+    - show me the list of names
     - What is the average age of people who donated?
     - What is the average donation amount?
     - What is the average donation of people older than 30?

pandas_llm/example.py

@@ -1,5 +1,9 @@
 import os
 import pandas as pd
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
 from pandas_llm import PandasLLM
 
 # Data
@@ -17,7 +21,7 @@
         ('Olivia Jackson', 29, 55)]
 df = pd.DataFrame(data, columns=['name', 'age', 'donation'])
 
-conv_df = PandasLLM(data=df, llm_api_key = os.environ.get("OPENAI_API_KEY"))
+conv_df = PandasLLM(data=df, llm_api_key = os.environ.get("OPENAI_API_KEY"), verbose=True)
 result = conv_df.prompt("What is the average donation of people older than 30 who donated more than $50?")
 
 print(f"Result ({type(result)}):\n {result}")