fixing github web rendering of the notebook

Author: ghd3v

end-to-end-use-cases/coding/text2sql/quickstart/quickstart.ipynb

@@ -5,8 +5,6 @@
       "id": "e8cba0b6",
       "metadata": {},
       "source": [
-        "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/coding/text2sql/quickstart/quickstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>  \n",
-        "\n",
         "## Quick Demo of Text2SQL Using Llama 3.3\n",
         "\n",
         "This demo shows how to use Llama 3.3 to answer questions about a SQLite DB. \n",
@@ -26,7 +24,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "id": "fa4562d3",
       "metadata": {},
       "outputs": [],
@@ -65,7 +63,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "id": "3bb99f39-cd7a-4db6-91dd-02f3bf80347c",
       "metadata": {},
       "outputs": [],
@@ -81,36 +79,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "id": "8d793ce7-324b-4861-926c-54973d7c9b43",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Based on the table schema below, write a SQL query that would answer the user's question; just return the SQL query and nothing else.\n",
-            "\n",
-            "Scheme:\n",
-            "\n",
-            "CREATE TABLE nba_roster (\n",
-            "\t\"Team\" TEXT, \n",
-            "\t\"NAME\" TEXT, \n",
-            "\t\"Jersey\" TEXT, \n",
-            "\t\"POS\" TEXT, \n",
-            "\t\"AGE\" INTEGER, \n",
-            "\t\"HT\" TEXT, \n",
-            "\t\"WT\" TEXT, \n",
-            "\t\"COLLEGE\" TEXT, \n",
-            "\t\"SALARY\" TEXT\n",
-            ")\n",
-            "\n",
-            "Question: What team is Stephen Curry on?\n",
-            "\n",
-            "SQL Query:\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "question = \"What team is Stephen Curry on?\"\n",
         "prompt = f\"\"\"Based on the table schema below, write a SQL query that would answer the user's question; just return the SQL query and nothing else.\n",
@@ -127,18 +99,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "id": "70776558",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "SELECT Team FROM nba_roster WHERE NAME = 'Stephen Curry'\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "answer = llm.invoke(prompt).content\n",
         "print(answer)"
@@ -154,21 +118,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "id": "62472ce6-794b-4a61-b88c-a1e031e28e4e",
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "\"[('Golden State Warriors',)]\""
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# note this is a dangerous operation and for demo purpose only; in production app you'll need to safe-guard any DB operation\n",
         "result = db.run(answer)\n",
@@ -177,18 +130,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": null,
       "id": "39ed4bc3",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "I don't have enough information to determine whose salary you are referring to. Could you please provide more context or specify the person you are asking about?\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# how about a follow up question\n",
         "follow_up = \"What's his salary?\"\n",
@@ -205,39 +150,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "id": "0c305278-29d2-4e88-9b3d-ad67c94ce0f2",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Based on the table schema, question, SQL query, and SQL response below, write a new SQL response; be concise, just output the SQL response.\n",
-            "\n",
-            "Scheme:\n",
-            "\n",
-            "CREATE TABLE nba_roster (\n",
-            "\t\"Team\" TEXT, \n",
-            "\t\"NAME\" TEXT, \n",
-            "\t\"Jersey\" TEXT, \n",
-            "\t\"POS\" TEXT, \n",
-            "\t\"AGE\" INTEGER, \n",
-            "\t\"HT\" TEXT, \n",
-            "\t\"WT\" TEXT, \n",
-            "\t\"COLLEGE\" TEXT, \n",
-            "\t\"SALARY\" TEXT\n",
-            ")\n",
-            "\n",
-            "Question: What's his salary?\n",
-            "SQL Query: What team is Stephen Curry on?\n",
-            "SQL Result: [('Golden State Warriors',)]\n",
-            "\n",
-            "New SQL Response:\n",
-            "\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "prompt = f\"\"\"Based on the table schema, question, SQL query, and SQL response below, write a new SQL response; be concise, just output the SQL response.\n",
         "\n",
@@ -255,18 +171,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
       "id": "03739b96-e607-4fa9-bc5c-df118198dc7f",
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "SELECT SALARY FROM nba_roster WHERE NAME = \"Stephen Curry\"\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "new_answer = llm.invoke(prompt).content\n",
         "print(new_answer)"
@@ -282,32 +190,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "id": "6ecfca53-be7e-4668-bad1-5ca7571817d7",
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "\"[('$51,915,615',)]\""
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "db.run(new_answer)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "9d79bbb1-e91d-4b56-b6ef-98c94ff414d0",
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {
@@ -331,5 +220,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.14"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }