Updating notebooks to use new Python SDK (#837)

examples/Function_calling_finding_nearby_places.ipynb

@@ -59,8 +59,11 @@
    "source": [
     "import json\n",
     "import openai\n",
+    "from openai import OpenAI\n",
     "import os\n",
-    "import requests"
+    "import requests\n",
+    "\n",
+    "client = OpenAI()\n"
    ]
   },
   {
@@ -141,7 +144,7 @@
     "    else:\n",
     "        print(f\"Google Place Details API request failed with status code {response.status_code}\")\n",
     "        print(f\"Response content: {response.content}\")\n",
-    "        return None"
+    "        return None\n"
    ]
   },
   {
@@ -179,21 +182,21 @@
     "            for place in results[:2]:  # limit to top 2 results\n",
     "                place_id = place.get(\"place_id\")\n",
     "                place_details = get_place_details(place_id, API_KEY)  # Get the details of the place\n",
-    "                \n",
+    "\n",
     "                place_name = place_details.get(\"name\", \"N/A\")\n",
     "                place_types = next((t for t in place_details.get(\"types\", []) if t not in [\"food\", \"point_of_interest\"]), \"N/A\")  # Get the first type of the place, excluding \"food\" and \"point_of_interest\"\n",
     "                place_rating = place_details.get(\"rating\", \"N/A\")  # Get the rating of the place\n",
     "                total_ratings = place_details.get(\"user_ratings_total\", \"N/A\")  # Get the total number of ratings\n",
     "                place_address = place_details.get(\"vicinity\", \"N/A\")  # Get the vicinity of the place\n",
-    "                \n",
+    "\n",
     "                if ',' in place_address:  # If the address contains a comma\n",
     "                    street_address = place_address.split(',')[0]  # Split by comma and keep only the first part\n",
     "                else:\n",
     "                    street_address = place_address\n",
-    "                \n",
+    "\n",
     "                # Prepare the output string for this place\n",
     "                place_info = f\"{place_name} is a {place_types} located at {street_address}. It has a rating of {place_rating} based on {total_ratings} user reviews.\"\n",
-    "                \n",
+    "\n",
     "                places.append(place_info)\n",
     "\n",
     "            return places\n",
@@ -203,7 +206,7 @@
     "            return []\n",
     "    except Exception as e:\n",
     "        print(f\"Error during the Google Places API call: {e}\")\n",
-    "        return []"
+    "        return []\n"
    ]
   },
   {
@@ -225,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,13 +236,13 @@
     "    customer_profile = fetch_customer_profile(user_id)\n",
     "    if customer_profile is None:\n",
     "        return \"I couldn't find your profile. Could you please verify your user ID?\"\n",
-    "    \n",
+    "\n",
     "    customer_profile_str = json.dumps(customer_profile)\n",
     "\n",
     "    food_preference = customer_profile.get('preferences', {}).get('food', [])[0] if customer_profile.get('preferences', {}).get('food') else None\n",
-    "  \n",
     "\n",
-    "    response = openai.ChatCompletion.create(\n",
+    "\n",
+    "    response = client.chat.completions.create(\n",
     "        model=\"gpt-3.5-turbo\",\n",
     "        messages=[\n",
     "    {\n",
@@ -272,10 +275,12 @@
     "        ],\n",
     "    )\n",
     "\n",
-    "    if 'message' in response.choices[0] and 'function_call' in response.choices[0]['message']:\n",
-    "        function_call = response.choices[0]['message']['function_call']\n",
-    "        if function_call[\"name\"] == \"call_google_places_api\":\n",
-    "            place_type = json.loads(function_call[\"arguments\"])[\"place_type\"]\n",
+    "    print(response.choices[0].message.function_call)\n",
+    "\n",
+    "    if response.choices[0].finish_reason=='function_call':\n",
+    "        function_call = response.choices[0].message.function_call\n",
+    "        if function_call.name == \"call_google_places_api\":\n",
+    "            place_type = json.loads(function_call.arguments)[\"place_type\"]\n",
     "            places = call_google_places_api(user_id, place_type, food_preference)\n",
     "            if places:  # If the list of places is not empty\n",
     "                return f\"Here are some places you might be interested in: {' '.join(places)}\"\n",
@@ -297,22 +302,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Here are some places you might be interested in: Mona Lisa Restaurant is a restaurant located at 353 Columbus Avenue #3907. It has a rating of 4.3 based on 1784 user reviews. Tommaso's Ristorante Italiano is a restaurant located at 1042 Kearny Street. It has a rating of 4.5 based on 732 user reviews.\n"
+      "FunctionCall(arguments='{\\n  \"place_type\": \"restaurant\"\\n}', name='call_google_places_api')\n",
+      "I couldn't find any places of interest nearby.\n"
      ]
     }
    ],
    "source": [
-    "user_id = \"user1234\"  \n",
-    "user_input = \"I'm hungry\"  \n",
+    "user_id = \"user1234\"\n",
+    "user_input = \"I'm hungry\"\n",
     "output = provide_user_specific_recommendations(user_input, user_id)\n",
-    "print(output)"
+    "print(output)\n"
    ]
   }
  ],
@@ -332,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4
  },