README fixed, EXAMPLES file added

Screenshots as a use case added, Jupyter Notebook added

EXAMPLES/Example.ipynb

@@ -0,0 +1,249 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "48250b17-aada-4838-8fe9-843fe970b904",
+      "metadata": {
+        "id": "48250b17-aada-4838-8fe9-843fe970b904"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "from IPython.display import Markdown, HTML, display"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "146be3c7-90df-4fbe-bff6-00166f3d61d2",
+      "metadata": {
+        "id": "146be3c7-90df-4fbe-bff6-00166f3d61d2"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# Replace with your actual values\n",
+        "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"INSERT THE OPENAI ENDPOINT\"\n",
+        "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"INSERT YOUR OPENAI API KEY\"\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f5e1b596-4568-4078-ae14-b20d25cba62b",
+      "metadata": {
+        "id": "f5e1b596-4568-4078-ae14-b20d25cba62b"
+      },
+      "outputs": [],
+      "source": [
+        "# 2nd Cell: Azure OpenAI setup\n",
+        "import os\n",
+        "from langchain_openai import AzureChatOpenAI\n",
+        "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+        "\n",
+        "# Load your Azure environment variables\n",
+        "AZURE_OPENAI_ENDPOINT = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+        "AZURE_DEPLOYMENT_NAME = \"gpt-4.1\"  # 👈 Change if needed\n",
+        "AZURE_API_VERSION = \"2025-01-01-preview\"  # 👈 Use your correct version\n",
+        "\n",
+        "# Define Azure LLM with streaming enabled\n",
+        "model = AzureChatOpenAI(\n",
+        "    openai_api_version=AZURE_API_VERSION,\n",
+        "    azure_deployment=AZURE_DEPLOYMENT_NAME,\n",
+        "    azure_endpoint=AZURE_OPENAI_ENDPOINT,\n",
+        "    streaming=True,\n",
+        "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "789b46d9-2189-4d3c-8f77-61b4675bf950",
+      "metadata": {
+        "id": "789b46d9-2189-4d3c-8f77-61b4675bf950"
+      },
+      "outputs": [],
+      "source": [
+        "# --- Setup ---\n",
+        "import os\n",
+        "import gradio as gr\n",
+        "import pandas as pd\n",
+        "import io\n",
+        "import contextlib\n",
+        "\n",
+        "from langchain.agents.agent_types import AgentType\n",
+        "from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n",
+        "\n",
+        "# Replace this with your actual LLM setup\n",
+        "# Example:\n",
+        "# from langchain_openai import AzureChatOpenAI\n",
+        "# model = AzureChatOpenAI(...)\n",
+        "\n",
+        "# Prompt\n",
+        "CSV_PROMPT_PREFIX = \"\"\"\n",
+        "Set pandas to show all columns.\n",
+        "Get the column names and infer data types.\n",
+        "Then attempt to answer the question using multiple methods.\n",
+        "Please provide only the Python code required to perform the action, and nothing else.\n",
+        "\"\"\"\n",
+        "\n",
+        "CSV_PROMPT_SUFFIX = \"\"\"\n",
+        "- Try at least 2 different methods of calculation or filtering.\n",
+        "- Reflect: Do they give the same result?\n",
+        "- After performing all necessary actions and analysis with the dataframe, return the answer in clean **Markdown**, include summary table if needed.\n",
+        "- Include **Execution Recommendation** and **Web Insight** in the final Markdown.\n",
+        "- Always conclude the final Markdown with:\n",
+        "\n",
+        "### Final Answer\n",
+        "\n",
+        "Your conclusion here.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Explanation\n",
+        "\n",
+        "Mention specific columns you used.\n",
+        "Please provide only the Python code required to perform the action, and nothing else until the final Markdown output.\n",
+        "\"\"\"\n",
+        "\n",
+        "# --- Agent Logic ---\n",
+        "def ask_agent(files, question):\n",
+        "    try:\n",
+        "        dfs = [pd.read_csv(f.name) for f in files]\n",
+        "        df = pd.concat(dfs, ignore_index=True)\n",
+        "    except Exception as e:\n",
+        "        return f\"❌ Could not read CSVs: {e}\", \"\"\n",
+        "\n",
+        "    try:\n",
+        "        agent = create_pandas_dataframe_agent(\n",
+        "        llm=model,\n",
+        "        df=df,\n",
+        "        verbose=True,\n",
+        "        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
+        "        allow_dangerous_code=True,\n",
+        "        handle_parsing_errors=True,  # 👈 this is the fix\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "        full_prompt = CSV_PROMPT_PREFIX + question + CSV_PROMPT_SUFFIX\n",
+        "\n",
+        "        buffer = io.StringIO()\n",
+        "        with contextlib.redirect_stdout(buffer):\n",
+        "            result = agent.invoke(full_prompt)\n",
+        "        trace = buffer.getvalue()\n",
+        "        output = result[\"output\"]\n",
+        "\n",
+        "\n",
+        "        return output, trace\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        return f\"❌ Agent error: {e}\", \"\"\n",
+        "\n",
+        "# --- Gradio UI ---\n",
+        "with gr.Blocks(\n",
+        "    css=\"\"\"\n",
+        "    body, .gradio-container {\n",
+        "        background: #ffffff !important;\n",
+        "        color: #1f2937 !important;\n",
+        "        font-family: 'Segoe UI', sans-serif;\n",
+        "    }\n",
+        "\n",
+        "    #title {\n",
+        "        color: #1f2937 !important;\n",
+        "        font-size: 2rem;\n",
+        "        font-weight: 600;\n",
+        "        text-align: center;\n",
+        "        padding-top: 20px;\n",
+        "        padding-bottom: 10px;\n",
+        "    }\n",
+        "\n",
+        "    .gr-box, .gr-input, .gr-output, .gr-markdown, .gr-textbox, .gr-file, textarea, input {\n",
+        "        background: rgba(0, 0, 0, 0.04) !important;\n",
+        "        border: 1px solid rgba(0, 0, 0, 0.1);\n",
+        "        border-radius: 12px !important;\n",
+        "        color: #1f2937 !important;\n",
+        "    }\n",
+        "\n",
+        "    textarea::placeholder, input::placeholder {\n",
+        "        color: rgba(31, 41, 55, 0.6) !important;\n",
+        "    }\n",
+        "\n",
+        "    button {\n",
+        "        background: rgba(0, 0, 0, 0.07) !important;\n",
+        "        color: #1f2937 !important;\n",
+        "        border: 1px solid rgba(0, 0, 0, 0.15) !important;\n",
+        "        border-radius: 8px !important;\n",
+        "    }\n",
+        "\n",
+        "    button:hover {\n",
+        "        background: rgba(0, 0, 0, 0.15) !important;\n",
+        "    }\n",
+        "    \"\"\"\n",
+        ") as demo:\n",
+        "\n",
+        "    gr.Markdown(\"<h2 id='title'>📊 NexDatawork Data Agent</h2>\")\n",
+        "\n",
+        "    with gr.Column():\n",
+        "        result_display = gr.Markdown(label=\"📌 Report Output (Markdown)\")\n",
+        "        trace_display = gr.Textbox(label=\"🛠️ Data Agent Reasoning - Your Explainable Agent\", lines=20)\n",
+        "\n",
+        "    with gr.Row(equal_height=True):\n",
+        "        file_input = gr.File(label=\"📁 Upload CSV(s)\", file_types=[\".csv\"], file_count=\"multiple\")\n",
+        "        question_input = gr.Textbox(\n",
+        "    label=\"💬 Ask Your Data\",\n",
+        "    placeholder=\"e.g., What is the trend for revenue over time?\",\n",
+        "    lines=9\n",
+        ")\n",
+        "\n",
+        "\n",
+        "    ask_button = gr.Button(\"💡 Analyze\")\n",
+        "\n",
+        "    ask_button.click(\n",
+        "        fn=ask_agent,\n",
+        "        inputs=[file_input, question_input],\n",
+        "        outputs=[result_display, trace_display]\n",
+        "    )\n",
+        "\n",
+        "demo.launch(share=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "fM4cO6jTgXlu"
+      },
+      "id": "fM4cO6jTgXlu",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.10 (LangChain)",
+      "language": "python",
+      "name": "langchain310"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.16"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

README.md

@@ -79,21 +79,51 @@ In the **Chat** tab you can ask the bot about the details of the data.
 After the analysis is completed the results are received in two tabs: **Data Brain** and **Dashboard**.
 
 ### Data Brain
-1) General overview of the data is presented as well as the methodology of approaching the dataset 
+1) General overview of the data is presented as well as the methodology of approaching the dataset
+
+<p align='center'>
+<image src='Images/executive_summary.png' alt='executive summary' width=500>
+</p>
 
 2) Recommendations on possible aspects of the data are generated
 
+<p align='center'>
+<image src='Images/business_insights.png' alt='business insights' width=500>
+</p>
+
 3) a conclusive overview of the data and statistical insights are presented
 
 
+
+<p align='center'>
+<image src='Images/Methodology.png' alt='Methodology' width=500 />
+<image src='Images/Data_evidence.png' alt='Data evidence' width=500 />
+<image src='Images/Statistical_insights.png' alt='Statistical insights' width=500 />
+<image src='Images/categorical_distribution.png' alt='categorical distribution' width=500 />
+</p>
+
+
 ### Dashboard
 
 Brief overview of the data with only the most important metrics and figures, such as:
  * file information
+<p align='center'>
+<image src='Images/file_information.png' alt='file_information' width=500 />
+</p>
+
  * number of columns of each type (numerical, categorical and temporal)
+
  * data quality and statistical summary
+<p align='center'>
+ <image src='Images/analysis_summary.png' alt='analysis_summary' width=500 />
+</p>
 
 Finally, graphs of the most important variables are presented.
+<p align='center'>
+ <image src='Images/graph1.png' alt='graph1' width=225 />
+ <image src='Images/graph2.png' alt='graph2' width=250 />
+ <image src='Images/graph3.png' alt='graph3' width=225 />
+</p>
 
 ## <a name='requirenments--starting-procedures'></a>Requirenments & Starting Procedures