error handling in Gemini call

library.ipynb

@@ -24,8 +24,11 @@
     "    # return response\n",
     "    genai.configure(api_key=key)\n",
     "    model = genai.GenerativeModel('gemini-pro')\n",
-    "    response = model.generate_content(text)\n",
-    "    return response.text"
+    "    try:\n",
+    "        response = model.generate_content(text)\n",
+    "    except Exception as e:\n",
+    "        return -1,str(e)\n",
+    "    return 0,response.text"
    ]
   },
   {

test.ipynb

@@ -1,367 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "from urllib.request import urlretrieve\n",
-    "import pandas as pd\n",
-    "import time\n",
-    "from allkeys import OPENAIKEY, GEMENIKEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import anvil.server\n",
-    "anvil.server.connect('PLMOIU5VCGGUOJH2XORIBWV3-ZXZVFLWX7QFIIAF4')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_result(task_id):\n",
-    "    while True:\n",
-    "        result=anvil.server.call('poll',task_id)\n",
-    "        if result!='In Progress' or result=='No such task': break\n",
-    "        else: \n",
-    "            time.sleep(1)\n",
-    "            print(result)\n",
-    "    print(result)\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text='write a python function to compute the nth digit of pi'\n",
-    "model='gpt-3.5-turbo'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "task_id=anvil.server.call('launch','call_gemini',text,GEMENIKEY)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "task_id=anvil.server.call('launch','call_gpt',text,OPENAIKEY,model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fetch_result(task_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(result[1],end='\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pathlib\n",
-    "import textwrap\n",
-    "from IPython.display import display\n",
-    "from IPython.display import Markdown\n",
-    "\n",
-    "def to_markdown(text):\n",
-    "  text = text.replace('•', '  *')\n",
-    "  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt='write code that defines a transformer network from scratch in pytorch'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response=anvil.server.call('call_gemini',prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "anvil.server.call('encode_anvil',prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "to_markdown(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "anvil.server.call('encode_anvil','I am a robot')[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode(text,server='local'):\n",
-    "    headers = {'Content-Type': 'application/json'}\n",
-    "    if server=='local': url='http://127.0.0.1:7860/encode'\n",
-    "    elif server=='hf': url='https://huggingface.co/spaces/gmshroff/gmserver/encode'\n",
-    "    body={'text':text}\n",
-    "    response=requests.post(url=url,data=json.dumps(body),headers = {'Content-Type': 'application/json'})\n",
-    "    return response\n",
-    "    return json.loads(response.content)['embedding']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response=encode('I am a robot',server='local')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response.content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "headers = {'Content-Type': 'application/json'}\n",
-    "# url='http://127.0.0.1:5000/run'\n",
-    "url='https://huggingface.co/spaces/gmshroff/gmserver/'\n",
-    "# url='http://127.0.0.1:7860/run'\n",
-    "# body={\"script\":\"python update_valdata.py\"}\n",
-    "# body={\"script\":\"pwd\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response=requests.get(url=url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response.content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# url='http://127.0.0.1:7860/encode'\n",
-    "body={'text':'I am very good'}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response=requests.post(url=url,data=json.dumps(body),headers = {'Content-Type': 'application/json'})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(response.__dict__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(json.loads(response.content)['embedding'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "urlretrieve(url='http://127.0.0.1:7860/file/data.csv',filename='./returned_file.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=pd.read_parquet('/tmp/validation_subset_int8.parquet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "\n",
-    "class Transformer(nn.Module):\n",
-    "    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):\n",
-    "        super(Transformer, self).__init__()\n",
-    "        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)\n",
-    "\n",
-    "    def forward(self, src, tgt):\n",
-    "        output = self.transformer(src, tgt)\n",
-    "        return output\n",
-    "\n",
-    "# Example usage:\n",
-    "# Define the model parameters\n",
-    "d_model = 512\n",
-    "nhead = 8\n",
-    "num_encoder_layers = 6\n",
-    "num_decoder_layers = 6\n",
-    "dim_feedforward = 2048\n",
-    "dropout = 0.1\n",
-    "\n",
-    "# Initialize the model\n",
-    "model = Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)\n",
-    "\n",
-    "# Generate some sample data\n",
-    "src = torch.rand(10, 32, 512)\n",
-    "tgt = torch.rand(20, 32, 512)\n",
-    "\n",
-    "# Pass the data through the model\n",
-    "output = model(src, tgt)\n",
-    "\n",
-    "# Print the output shape\n",
-    "print(output.shape)"
-   ]
-  }
- ],
- "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}