From e8ddcea4d791efe87b2e8deff972a470082978d8 Mon Sep 17 00:00:00 2001
From: Prathamesh-Udoshi <prathameshudoshi@gmail.com>
Date: Tue, 22 Jul 2025 00:50:00 +0530
Subject: [PATCH] Added Movie_Suggestion using Ollama in Community
 Contributions

---
 .../Movie_Suggestion.ipynb                    | 104 ++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 week1/community-contributions/Movie_Suggestion.ipynb

diff --git a/week1/community-contributions/Movie_Suggestion.ipynb b/week1/community-contributions/Movie_Suggestion.ipynb
new file mode 100644
index 0000000..930d5b0
--- /dev/null
+++ b/week1/community-contributions/Movie_Suggestion.ipynb
@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7bb9010e-48a8-491e-a2a9-1a8dacc26f87",
+   "metadata": {},
+   "source": [
+    "# Movie Suggestion using Ollama Running Locally\n",
+    "\n",
+    "#### Takes the user input like languages and Genre and suggests Top 10 Movies of the selected attributes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad049302-dce8-4a0a-88ab-e485ac15fbe4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from IPython.display import display, Markdown\n",
+    "\n",
+    "def get_movie_recommendations(language, genre, top_n=10, model='llama3.2'):\n",
+    "    api_url = \"http://localhost:11434/api/generate\"\n",
+    "    prompt = (\n",
+    "        f\"Recommend {top_n} well-rated {language} movies from the {genre} genre. \"\n",
+    "        \"For each movie, provide the name and a 1-2 sentence preview of its story. \"\n",
+    "        \"Return the results as a Markdown table with columns: Title, Short Summary.\"\n",
+    "    )\n",
+    "    data = {\n",
+    "        \"model\": model,\n",
+    "        \"prompt\": prompt,\n",
+    "        \"options\": {\"num_predict\": 800},\n",
+    "        \"stream\": False\n",
+    "    }\n",
+    "    response = requests.post(api_url, json=data)\n",
+    "    # Extract text response (could be markdown table already)\n",
+    "    return response.json().get(\"response\", \"\").strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01400553-419c-4798-8f19-e32e49379761",
+   "metadata": {},
+   "source": [
+    "#### Enter your Language and Genre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7527230-1e10-4b67-94c0-a84519b256c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language = input(\"Enter preferred language (e.g., French, Japanese): \").strip()\n",
+    "genre = input(\"Enter preferred genre (e.g., Drama, Comedy, Thriller): \").strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ff0146f-b37e-4218-9678-15a40bed3659",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recommendations_md = get_movie_recommendations(language, genre)\n",
+    "# This prints out the Markdown table as formatted by the Llama 3.2 model\n",
+    "from IPython.display import display, Markdown\n",
+    "\n",
+    "display(Markdown(recommendations_md))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58cc0fa4-a2a6-4597-8ae9-39970fb2a7b5",
+   "metadata": {},
+   "source": [
+    "### The Result will be displayed in a markdown fashion in a neat table with rows and columns."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}