lfs and style

.gitattributes

@@ -1 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
 *.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,110 +1,12 @@
-# Data Analysis Agent Interface with Streamlit
-
-This Streamlit application provides an interface for interacting with a data analysis agent powered by OpenAI's language models. It allows users to ask questions about data in a CSV file and receive answers in the form of Pandas code, data tables, and visualizations.  The application also supports generating a PDF report of the analysis.
-
-## Features
-
-*   **Natural Language Queries:** Ask questions in plain English (or Romanian) about the data.
-*   **Automatic Code Generation:** The agent generates Pandas code to answer the query.
-*   **Data Display:** Results are displayed as interactive DataFrames.
-*   **Visualization:**  Generates various plots (bar charts, pie charts, histograms, heatmaps, scatter plots, line plots, box plots, violin plots, area charts, and radar charts) based on the query and data.
-*   **PDF Report Generation:**  Download a PDF report containing the query, generated code, data table, and plots.
-*   **Syntax-Highlighted Code:**  The generated Python code is displayed in a scrollable, syntax-highlighted code block for easy readability.
-*   **Collapsible Code Display:** The generated code is hidden by default, with an expander to reveal it on demand.
-*   **Sample Questions:**  Provides a set of sample questions to get started.
-*   **Powered by ZEGA.ai:**  Includes ZEGA.ai branding.
-
-## Getting Started
-
-### Prerequisites
-
-*   Python 3.7+
-*   An OpenAI API key
-*   pdfkit: you need to have wkhtmltopdf installed on your system.
-    *   **Windows**: Download and install from [wkhtmltopdf.org](https://wkhtmltopdf.org/downloads.html). Add the `wkhtmltopdf/bin` directory to your system's PATH.
-    *   **macOS**: `brew install wkhtmltopdf`
-    *   **Linux (Debian/Ubuntu)**: `sudo apt-get install wkhtmltopdf`
-    *   **Linux (CentOS/RHEL)**: `sudo yum install wkhtmltopdf`
-
-### Installation
-
-1.  **Clone the repository:**
-
-    ```bash
-    git clone <your_repository_url>
-    cd <your_repository_directory>
-    ```
-
-2.  **Install dependencies:**
-
-    ```bash
-    pip install -r requirements.txt
-    ```
-    Create the `requirements.txt` and place this in:
-    ```
-    streamlit
-    pandas
-    matplotlib
-    plotly
-    python-dotenv
-    langchain
-    langchain-experimental
-    langchain-openai
-    seaborn
-    pdfkit
-    openai
-    ```
-
-3.  **Create a `.env` file:**
-
-    Create a file named `.env` in the root directory of your project.  Add your OpenAI API key to this file:
-
-    ```
-    OPENAI_API_KEY=your_openai_api_key_here
-    ```
-    Replace `your_openai_api_key_here` with your actual API key.
-
-4.  **Place the CSV data file:**
-
-    Place the `asig_sales_31012025.csv` file in the same directory as your script.  If you use a different CSV file, update the `csv_path` variable in the script.
-
-5. **Place Zega logo**
-    Place the `zega_logo.png` into the folder.
-
-### Usage
-
-1.  **Run the Streamlit app:**
-
-    ```bash
-    streamlit run your_script_name.py
-    ```
-    Replace `your_script_name.py` with the name of your Python script.
-
-2.  **Interact with the app:**
-
-    *   Select a sample question from the sidebar or enter your own question in the text area.  Ensure you ask only one question at a time.
-    *   Click the "Submit" button.
-    *   The results (data table and plots) will be displayed.
-    *   Click the "Show the code" expander to view the generated Pandas code.
-    *   Click the "Download PDF" button to generate a PDF report.
-
-## File Structure
-
-*   **`your_script_name.py`:**  The main Streamlit application script.
-*   **`.env`:**  Contains your OpenAI API key (should *not* be committed to Git).
-*   **`requirements.txt`:**  Lists the required Python packages.
-*   **`asig_sales_31012025.csv`:**  The CSV data file (or your custom data file).
-*  **`zega_logo.png`:**  Zega logo.
-*   **`exported_pdfs/`:**  A directory (created automatically) where generated PDF reports are saved.
-*   **`README.md`:** This file.
-
-## Important Notes
-
-*   **Date Format:** The script is specifically configured to handle dates in the European DD/MM/YYYY format.  Ensure your CSV data uses this format.  The `parse_dates` argument in `pd.read_csv` is crucial for correct date handling.
-*   **OpenAI API Key:** Keep your OpenAI API key secure.  Do *not* commit the `.env` file to your Git repository. Add `.env` to your `.gitignore` file.
-*   **Error Handling:** The script includes basic error handling (checking for the CSV file), but you might want to add more robust error handling for production use.
-*   **wkhtmltopdf:** Ensure `wkhtmltopdf` is correctly installed and accessible in your system's PATH for PDF generation to work.
-*   **Prompt Engineering:**  The quality of the generated code depends heavily on the prompt used in the `generate_code` function.  The provided prompt is highly detailed and includes specific instructions for the agent.  You may need to adjust the prompt if you encounter issues or use a different CSV file with different column names or data structures.
-* **One Question:** The app is designed to process one question at a time. Asking multiple questions in a single input may lead to unexpected behavior.
-
-
+---
+title: DataAnalystDemo
+emoji: 👁
+colorFrom: pink
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.44.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

style.css

@@ -29,14 +29,17 @@ h4 {
 
 /* Butoane */
 
-button {
+button,
+.myButton {
 	font-weight: 600 !important;
 }
-button:hover {
+button:hover,
+.myButton:hover {
 	color: #FA6C03 !important;
 	border-color: #FA6C03 !important;
 }
-button:focus {
+button:focus,
+.myButton:focus {
 	color: #fff !important;
 	background-color: #FA6C03 !important;
 }
@@ -57,7 +60,8 @@ button:focus {
 }
 
 /* Select */
-.st-ae {
+.st-ae,
+.st-az {
 	font-family: inherit !important;
 }
 
@@ -67,6 +71,6 @@ button:focus {
 }
 
 /* Other fonts */
-p {
-	font-family: 'Inter Tight', Arial, Helvetica, sans-serif !important;
+[class^="st-emotion-cache-"] {
+  font-family: 'Inter Tight', Arial, Helvetica, sans-serif !important;
 }