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| import os | |
| import pandas as pd | |
| import gradio as gr | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_experimental.agents import create_pandas_dataframe_agent | |
| from langchain_core.prompts import PromptTemplate | |
| # Set up API key for Google Gemini | |
| os.environ["GOOGLE_API_KEY"] = "AIzaSyDSorjiEVV2KCWelkDLFxQsju3KDQOF344" # Replace with actual API key | |
| # Initialize the LLM | |
| llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") | |
| # Placeholder for agent and dataframe | |
| agent = None | |
| df = None | |
| # Define the function to handle CSV uploads and set up the LangChain agent | |
| def handle_file_upload(file): | |
| global agent, df | |
| # Check if file has .csv extension | |
| if not file.name.endswith(".csv"): | |
| return "Error: Please upload a valid CSV file.", None | |
| # Load the uploaded file into a DataFrame | |
| try: | |
| df = pd.read_csv(file) # Read directly from the file object | |
| # Create a new LangChain agent with the uploaded DataFrame | |
| agent = create_pandas_dataframe_agent(llm, df, verbose=True, allow_dangerous_code=True) | |
| return "CSV uploaded successfully. You can now ask questions about the data.", df | |
| except Exception as e: | |
| return f"Error reading CSV file: {e}", None | |
| # Define the function to process the user query | |
| def answer_query(query): | |
| if agent is None: | |
| return "Please upload a CSV file first." | |
| # Invoke the agent with the query | |
| formatted_query = PromptTemplate.from_template( | |
| ''' | |
| Please act as a data analyst and respond to my queries with insights from the provided dataset. | |
| If your response involves numeric data or comparisons, format the answer in a clear tabular form whenever | |
| it enhances readability and clarity. | |
| Provide analyses that highlight trends, patterns, and notable details in the data, and use tabular format | |
| for presenting summaries, comparisons, or grouped data and whenever user asks listing or something similar | |
| to help illustrate your findings effectively. Additionally, interpret any findings with context and data-driven | |
| reasoning as a skilled data analyst would. Also make sure not to give any data that is not asked by the user or | |
| not relevant to the given context | |
| Keep the above said details in mind and answer the below query: | |
| Query: | |
| {query} | |
| ''' | |
| ) | |
| response = agent.invoke(query) | |
| # Check if the response contains tabular data | |
| if isinstance(response, pd.DataFrame): | |
| return response # Display as table if it's a DataFrame | |
| else: | |
| # Format response as Markdown | |
| return f"**Response:**\n\n{response['output']}" | |
| # Create the Gradio interface | |
| with gr.Blocks() as iface: | |
| gr.Markdown("# ZEN-Analyser") | |
| gr.Markdown("Upload a CSV file to view the data and ask questions about it.") | |
| # File upload component | |
| file_input = gr.File(label="Upload CSV", file_types=[".csv"]) | |
| # Dataframe display for the uploaded CSV | |
| data_output = gr.DataFrame(label="Uploaded Data") | |
| # Textbox for entering queries | |
| query_input = gr.Textbox(label="Enter your query") | |
| # Markdown component for displaying the agent's response with Markdown support | |
| response_output = gr.Markdown(label="Response") | |
| # Button to trigger query processing | |
| query_button = gr.Button("Submit Query") | |
| # Define event actions | |
| file_input.upload(handle_file_upload, file_input, [response_output, data_output]) | |
| query_button.click(answer_query, query_input, response_output) | |
| # Launch the Gradio app | |
| iface.launch() | |