migration from openai to groq/compound-mini

.gitignore

@@ -1,5 +1,5 @@
 # ignore virtual environment
-chartbot/
+.venv/
 
 # ignore python cache
 __pycache__/

app.py

@@ -131,4 +131,4 @@ def update_navlink_styles(pathname):
     ]
 
 if __name__ == "__main__":
-    app.run_server(debug=False)
+    app.run(debug=False)

pages/amazon_purchases_chartbot.py

@@ -1,5 +1,6 @@
 import dash
-from dash import callback, Input, Output, State, ctx
+from dash import callback, Input, Output, State, ctx, html
+import dash_mantine_components as dmc
 
 from utils import chartbot_dataset_layout, prompt, helpers
 
@@ -71,12 +72,20 @@ def update_submit_loading(n_clicks):
     prevent_initial_call=True
 )
 def create_graph(_, user_prompt):
-    df_5_rows = df.head(5)
-    data_top5_csv_string = df_5_rows.to_csv(index=False)
+    try:
+        df_5_rows = df.head(5)
+        data_top5_csv_string = df_5_rows.to_csv(index=False)
 
-    result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
+        result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
 
-    return helpers.display_response(result_output, DATA_FILE_PATH)
+        return helpers.display_response(result_output, DATA_FILE_PATH)
+    
+    except Exception as e:
+        error_message = str(e)
+        return html.Div([
+            html.Br(),
+            dmc.Alert(error_message, title="Error", color="red")
+        ]), None, {"display": "none"}, None, False
 
 @callback(
     Output("amazon-purchases-download-html", "href"),

pages/chartbot.py

@@ -112,15 +112,23 @@ def create_graph(n_clicks, user_prompt, file_data, file_name):
         return dash.no_update, dash.no_update, dash.no_update, dash.no_update, dash.no_update
     
     else:
-        df = pd.DataFrame(file_data)
-        df_5_rows = df.head(5)
-        data_top5_csv_string = df_5_rows.to_csv(index=False)
-
-        helpers.save_dataframe_to_current_path(df, file_name)
-
-        result_output = prompt.get_response(user_prompt, data_top5_csv_string, file_name)
-
-        return helpers.display_response(result_output, file_name)
+        try:
+            df = pd.DataFrame(file_data)
+            df_5_rows = df.head(5)
+            data_top5_csv_string = df_5_rows.to_csv(index=False)
+
+            helpers.save_dataframe_to_current_path(df, file_name)
+
+            result_output = prompt.get_response(user_prompt, data_top5_csv_string, file_name)
+
+            return helpers.display_response(result_output, file_name)
+        
+        except Exception as e:
+            error_message = str(e)
+            return html.Div([
+                html.Br(),
+                dmc.Alert(error_message, title="Error", color="red")
+            ]), None, {"display": "none"}, None, False
 
 @callback(
     Output("download-html", "href"),

pages/ewf_chartbot.py

@@ -1,5 +1,6 @@
 import dash
-from dash import callback, Input, Output, State, ctx
+from dash import callback, Input, Output, State, ctx, html
+import dash_mantine_components as dmc
 
 from utils import chartbot_dataset_layout, prompt, helpers
 
@@ -69,12 +70,20 @@ def update_submit_loading(n_clicks):
     prevent_initial_call=True
 )
 def create_graph(_, user_prompt):
-    df_5_rows = df.head(5)
-    data_top5_csv_string = df_5_rows.to_csv(index=False)
+    try:
+        df_5_rows = df.head(5)
+        data_top5_csv_string = df_5_rows.to_csv(index=False)
 
-    result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
+        result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
 
-    return helpers.display_response(result_output, DATA_FILE_PATH)
+        return helpers.display_response(result_output, DATA_FILE_PATH)
+    
+    except Exception as e:
+        error_message = str(e)
+        return html.Div([
+            html.Br(),
+            dmc.Alert(error_message, title="Error", color="red")
+        ]), None, {"display": "none"}, None, False
 
 @callback(
     Output("ewf-download-html", "href"),

pages/space_missions_chartbot.py

@@ -1,5 +1,6 @@
 import dash
-from dash import callback, Input, Output, State, ctx
+from dash import callback, Input, Output, State, ctx, html
+import dash_mantine_components as dmc
 
 from utils import chartbot_dataset_layout, prompt, helpers
 
@@ -69,12 +70,20 @@ def update_submit_loading(n_clicks):
     prevent_initial_call=True
 )
 def create_graph(_, user_prompt):
-    df_5_rows = df.head(5)
-    data_top5_csv_string = df_5_rows.to_csv(index=False)
+    try:
+        df_5_rows = df.head(5)
+        data_top5_csv_string = df_5_rows.to_csv(index=False)
 
-    result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
+        result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
 
-    return helpers.display_response(result_output, DATA_FILE_PATH)
+        return helpers.display_response(result_output, DATA_FILE_PATH)
+    
+    except Exception as e:
+        error_message = str(e)
+        return html.Div([
+            html.Br(),
+            dmc.Alert(error_message, title="Error", color="red")
+        ]), None, {"display": "none"}, None, False
 
 @callback(
     Output("space-missions-download-html", "href"),

pages/us_elections_chartbot.py

@@ -1,5 +1,6 @@
 import dash
-from dash import callback, Input, Output, State, ctx
+from dash import callback, Input, Output, State, ctx, html
+import dash_mantine_components as dmc
 
 from utils import chartbot_dataset_layout, prompt, helpers
 
@@ -72,12 +73,20 @@ def update_submit_loading(n_clicks):
     prevent_initial_call=True
 )
 def create_graph(_, user_prompt):
-    df_5_rows = df.head(5)
-    data_top5_csv_string = df_5_rows.to_csv(index=False)
+    try:
+        df_5_rows = df.head(5)
+        data_top5_csv_string = df_5_rows.to_csv(index=False)
 
-    result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
+        result_output = prompt.get_response(user_prompt, data_top5_csv_string, DATA_FILE_PATH)
 
-    return helpers.display_response(result_output, DATA_FILE_PATH)
+        return helpers.display_response(result_output, DATA_FILE_PATH)
+    
+    except Exception as e:
+        error_message = str(e)
+        return html.Div([
+            html.Br(),
+            dmc.Alert(error_message, title="Error", color="red")
+        ]), None, {"display": "none"}, None, False
 
 @callback(
     Output("us-elections-download-html", "href"),

utils/helpers.py

@@ -16,7 +16,7 @@ from utils import prompt
 
 # Function to get the path of a file in the app source code
 def get_app_file_path(directory_name: str, file_name: str) -> str:
-    return os.path.join(os.path.dirname(__file__), "..\\{}".format(directory_name), file_name)
+    return os.path.join(os.path.dirname(__file__), "..", directory_name, file_name)
 
 # Function to read the content of a file
 def read_doc(file_path: str) -> str:
@@ -34,37 +34,50 @@ def get_fig_from_code(code, file_name):
         exec(code, {}, local_variables)
 
     except Exception as e:
-        result_output = prompt.get_python_exception_response(code, str(e))
-        return display_response(result_output, file_name)
+        try:
+            result_output = prompt.get_python_exception_response(code, str(e))
+            return display_response(result_output, file_name)
+        except Exception as api_error:
+            # If the API call fails, raise the error to be handled by display_response
+            raise api_error
 
     return local_variables["fig"]
 
 def display_response(response, file_name):
-    code_block_match = re.search(r"```(?:[Pp]ython)?(.*?)```", response, re.DOTALL)
-    #print(code_block_match)
-
-    if code_block_match:
-        code_block = code_block_match.group(1).strip()
-        cleaned_code = re.sub(r'(?m)^\s*fig\.show\(\)\s*$', '', code_block)
-
-        try:
-            fig = get_fig_from_code(cleaned_code, file_name)
-
-            buffer = io.StringIO()
-            fig.write_html(buffer)
-            html_bytes = buffer.getvalue().encode()
-            encoded = b64encode(html_bytes).decode()
-
-            return dcc.Graph(figure=fig), response, {"display": "block"}, encoded, False
-
-        except AttributeError as e:
-            return html.Div([
-                html.Br(),
-                dmc.Alert("The code generated has errors, please try again.", title="Error", color="red")
-            ]), None, {"display": "none"}, None, False
-    
-    else:
-        return "", response, {"display": "none"}, None, False
+    try:
+        code_block_match = re.search(r"```(?:[Pp]ython)?(.*?)```", response, re.DOTALL)
+        #print(code_block_match)
+
+        if code_block_match:
+            code_block = code_block_match.group(1).strip()
+            cleaned_code = re.sub(r'(?m)^\s*fig\.show\(\)\s*$', '', code_block)
+
+            try:
+                fig = get_fig_from_code(cleaned_code, file_name)
+
+                buffer = io.StringIO()
+                fig.write_html(buffer)
+                html_bytes = buffer.getvalue().encode()
+                encoded = b64encode(html_bytes).decode()
+
+                return dcc.Graph(figure=fig), response, {"display": "block"}, encoded, False
+
+            except AttributeError as e:
+                return html.Div([
+                    html.Br(),
+                    dmc.Alert("The code generated has errors, please try again.", title="Error", color="red")
+                ]), None, {"display": "none"}, None, False
+        
+        else:
+            return "", response, {"display": "none"}, None, False
+            
+    except Exception as e:
+        # Handle API errors gracefully
+        error_message = str(e)
+        return html.Div([
+            html.Br(),
+            dmc.Alert(error_message, title="API Error", color="red")
+        ]), None, {"display": "none"}, None, False
 
 # Function to parse the contents of the uploaded file
 def parse_contents(contents, filename):

utils/prompt.py

@@ -3,26 +3,32 @@ import os
 from dotenv import load_dotenv
 
 # libraries to help with the AI model
-from langchain_openai import AzureChatOpenAI
+from langchain_groq import ChatGroq
 from langchain_core.messages import HumanMessage
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
 from utils import helpers
 
 # get the credentials from .env
 load_dotenv()
-AZURE_KEY = os.getenv('KEY')
-AZURE_ENDPOINT = os.getenv('LLM_ENDPOINT')
-AZURE_NAME = os.getenv('LLM_DEPLOYMENT_NAME')
-AZURE_VERSION = os.getenv('VERSION')
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+
+# Validate that the API key is present and not a placeholder
+if not GROQ_API_KEY or GROQ_API_KEY == 'your_groq_api_key_here':
+    raise ValueError(
+        "GROQ_API_KEY environment variable is not set or is still the placeholder. "
+        "Please update the .env file with your actual Groq API key."
+    )
 
 # define connectivity to the llm
-llm = AzureChatOpenAI(
-    deployment_name=AZURE_NAME, 
-    openai_api_version=AZURE_VERSION, 
-    openai_api_key=AZURE_KEY, 
-    azure_endpoint=AZURE_ENDPOINT
-)
+try:
+    llm = ChatGroq(
+        model="groq/compound-mini",
+        api_key=GROQ_API_KEY,
+        temperature=0
+    )
+except Exception as e:
+    raise ValueError(f"Failed to initialize ChatGroq: {str(e)}")
 
 '''Before creating any visualizations, ensure that any rows with NaN or missing values in the relevant columns are removed. Additionally, 
             handle missing values appropriately based on the context, ensuring cleaner visualizations.
@@ -68,55 +74,106 @@ def get_prompt_text() -> str:
             Here are the first 5 rows of the data set: {data}. Follow the user's indications when creating the graph.
             There should be no natural language text in the python code block."""
 
-def get_response(user_input: str, data_top5_csv_string: str, file_name: str) -> None:
-    prompt = ChatPromptTemplate.from_messages(
-            [
-                (
-                    "system",
-                    get_prompt_text()
-                ),
-                MessagesPlaceholder(variable_name="messages")
-            ]
-        )
+def get_response(user_input: str, data_top5_csv_string: str, file_name: str) -> str:
+    """
+    Get a response from the LLM for creating data visualizations.
+    
+    Args:
+        user_input: User's request for visualization
+        data_top5_csv_string: CSV string of first 5 rows of data
+        file_name: Name of the data file
+        
+    Returns:
+        LLM response content
+        
+    Raises:
+        Exception: If API call fails
+    """
+    try:
+        prompt = ChatPromptTemplate.from_messages(
+                [
+                    (
+                        "system",
+                        get_prompt_text()
+                    ),
+                    MessagesPlaceholder(variable_name="messages")
+                ]
+            )
 
-    chain = prompt | llm
-
-    response = chain.invoke(
-        {
-            "messages": [HumanMessage(content=user_input)],
-            "data_visualization_best_practices": helpers.read_doc(helpers.get_app_file_path("assets", "data_viz_best_practices.txt")),
-            "example_subplots1": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots1.txt")),
-            "example_subplots2": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots2.txt")),
-            "example_subplots3": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots3.txt")),
-            "data": data_top5_csv_string,
-            "name_of_file": file_name
-        }
-    )
+        chain = prompt | llm
 
-    return response.content
+        response = chain.invoke(
+            {
+                "messages": [HumanMessage(content=user_input)],
+                "data_visualization_best_practices": helpers.read_doc(helpers.get_app_file_path("assets", "data_viz_best_practices.txt")),
+                "example_subplots1": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots1.txt")),
+                "example_subplots2": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots2.txt")),
+                "example_subplots3": helpers.read_doc(helpers.get_app_file_path("assets", "example_subplots3.txt")),
+                "data": data_top5_csv_string,
+                "name_of_file": file_name
+            }
+        )
+
+        return response.content
+    
+    except Exception as e:
+        error_msg = str(e)
+        if "rate_limit" in error_msg.lower() or "429" in error_msg:
+            raise Exception("Rate limit exceeded. Please wait a moment and try again.")
+        elif "authentication" in error_msg.lower() or "401" in error_msg or "api_key" in error_msg.lower():
+            raise Exception("Authentication failed. Please check your GROQ_API_KEY in the .env file.")
+        elif "timeout" in error_msg.lower():
+            raise Exception("Request timed out. Please try again.")
+        else:
+            raise Exception(f"Error communicating with Groq API: {error_msg}")
 
 def get_python_exception_prompt_text() -> str:
     return """The Python code you provided {code} has an error {exception}"""
 
-def get_python_exception_response(code: str, exception: str) -> None:
-    prompt = ChatPromptTemplate.from_messages(
-            [
-                (
-                    "system",
-                    get_python_exception_prompt_text()
-                ),
-                MessagesPlaceholder(variable_name="messages")
-            ]
-        )
+def get_python_exception_response(code: str, exception: str) -> str:
+    """
+    Get a response from the LLM to fix Python code errors.
+    
+    Args:
+        code: The Python code that has errors
+        exception: The exception message
+        
+    Returns:
+        LLM response with fixed code
+        
+    Raises:
+        Exception: If API call fails
+    """
+    try:
+        prompt = ChatPromptTemplate.from_messages(
+                [
+                    (
+                        "system",
+                        get_python_exception_prompt_text()
+                    ),
+                    MessagesPlaceholder(variable_name="messages")
+                ]
+            )
 
-    chain = prompt | llm
+        chain = prompt | llm
 
-    response = chain.invoke(
-        {
-            "messages": [HumanMessage(content="Rewrite the entire Python code so that it does not contain any errors. The code should be able to run without any errors.")],
-            "code": code,
-            "exception": exception
-        }
-    )
+        response = chain.invoke(
+            {
+                "messages": [HumanMessage(content="Rewrite the entire Python code so that it does not contain any errors. The code should be able to run without any errors.")],
+                "code": code,
+                "exception": exception
+            }
+        )
 
-    return response.content
+        return response.content
+    
+    except Exception as e:
+        error_msg = str(e)
+        if "rate_limit" in error_msg.lower() or "429" in error_msg:
+            raise Exception("Rate limit exceeded. Please wait a moment and try again.")
+        elif "authentication" in error_msg.lower() or "401" in error_msg or "api_key" in error_msg.lower():
+            raise Exception("Authentication failed. Please check your GROQ_API_KEY in the .env file.")
+        elif "timeout" in error_msg.lower():
+            raise Exception("Request timed out. Please try again.")
+        else:
+            raise Exception(f"Error communicating with Groq API: {error_msg}")