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| import streamlit as st | |
| import pandas as pd | |
| import google.generativeai as genai | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() # Load environment variables from .env file if it exists | |
| # Function to get Gemini API key (you can set it in Streamlit secrets or environment variables) | |
| def get_gemini_api_key(): | |
| api_key = os.getenv("GOOGLE_API_KEY") # Try environment variable first | |
| if not api_key: | |
| api_key = st.secrets.get("GOOGLE_API_KEY") # Then try Streamlit secrets | |
| return api_key | |
| # Function to analyze data with Gemini | |
| def analyze_csv_with_gemini(csv_file, analysis_request): | |
| """ | |
| Analyzes CSV data using the Gemini Pro model. | |
| Args: | |
| csv_file: Uploaded CSV file object. | |
| analysis_request: User's text request for analysis. | |
| Returns: | |
| str: Gemini's analysis response. | |
| """ | |
| try: | |
| df = pd.read_csv(csv_file) | |
| csv_string = df.to_csv(index=False) # Convert DataFrame to string for Gemini prompt | |
| api_key = get_gemini_api_key() | |
| if not api_key: | |
| st.error("Gemini API key not found. Please set it as GOOGLE_API_KEY environment variable or in Streamlit secrets.") | |
| return None | |
| genai.configure(api_key=api_key) | |
| model = genai.GenerativeModel('gemini-2.0-flash-thinking-exp-01-21') | |
| prompt_content = f""" | |
| You are a data analysis expert. Analyze the following CSV data and provide insights based on the user's request. | |
| CSV Data: | |
| ```csv | |
| {csv_string} | |
| ``` | |
| Analysis Request: {analysis_request} | |
| Please provide your analysis in a clear, concise, and informative way. | |
| Focus on identifying key trends, patterns, and insights from the data that are relevant to the request. | |
| If possible, suggest potential next steps or questions for further investigation based on your analysis. | |
| """ | |
| response = model.generate_content(prompt_content) | |
| return response.text | |
| except pd.errors.ParserError: | |
| return "Error: Could not parse the CSV file. Please ensure it is a valid CSV format." | |
| except Exception as e: | |
| return f"An error occurred during analysis: {e}" | |
| # Streamlit App | |
| st.title("CSV Data Analyzer with Gemini") | |
| st.write("Upload your CSV file and enter your analysis request below.") | |
| uploaded_file = st.file_uploader("Upload CSV file", type=["csv"]) | |
| analysis_request = st.text_area("Enter your analysis request (e.g., 'Summarize key trends', 'Identify correlations', 'Find outliers', 'Give me top 5 insights'):", height=150) | |
| if st.button("Analyze"): | |
| if uploaded_file is not None and analysis_request: | |
| st.info("Analyzing data with Gemini... Please wait.") | |
| gemini_response = analyze_csv_with_gemini(uploaded_file, analysis_request) | |
| if gemini_response: | |
| st.subheader("Gemini Analysis Results:") | |
| st.markdown(gemini_response) | |
| else: | |
| st.error("Failed to get analysis from Gemini. Please check API key and try again.") | |
| elif uploaded_file is None: | |
| st.warning("Please upload a CSV file.") | |
| elif not analysis_request: | |
| st.warning("Please enter your analysis request.") | |
| # Instructions and API Key Setup | |
| st.sidebar.header("Instructions") | |
| st.sidebar.markdown( | |
| """ | |
| 1. **Upload CSV File:** Use the file uploader to upload your CSV file. | |
| 2. **Enter Analysis Request:** Type your specific data analysis request in the text area. Be clear about what insights you are looking for. | |
| 3. **Click 'Analyze':** Press the 'Analyze' button to start the analysis using Gemini. | |
| 4. **View Results:** The Gemini analysis will be displayed below. | |
| **API Key Setup:** | |
| **Important Notes:** | |
| * This application uses the gemini-2.0-flash-thinking-exp-01-21 model. | |
| * The performance depends on the complexity of your data and analysis request. | |
| * For very large CSV files, consider sampling or preprocessing the data before analysis for faster results on CPU-only machines. | |
| """ | |
| ) |