{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5c9cf754",
"metadata": {},
"outputs": [],
"source": [
"import modal"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a73b738f",
"metadata": {},
"outputs": [],
"source": [
"import modal\n",
"\n",
"app = modal.App(\"example-get-started\")\n",
"\n",
"\n",
"@app.function()\n",
"def square(x):\n",
" print(\"This code is running on a remote worker!\")\n",
" return x**2\n",
"\n",
"\n",
"@app.local_entrypoint()\n",
"def main():\n",
" print(\"the square is\", square.remote(42))\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f8653ac7",
"metadata": {},
"outputs": [],
"source": [
"from hello import app, hello"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae58c3a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello from Delhi, Delhi, IN!!'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from hello import app, hello\n",
" reply=hello.local()\n",
"reply"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9f178389",
"metadata": {},
"outputs": [],
"source": [
"from llama import app, generate"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "03715973",
"metadata": {},
"outputs": [
{
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"Output()"
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"✓ Initialized. View run at https://modal.com/apps/ankushmehta243/main/ap-eokiQPPBn1jbB7S6coAKUc\n",
"\n"
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"\u001b[32m✓\u001b[0m Initialized. \u001b[38;5;249mView run at \u001b[0m\u001b[4;38;5;249mhttps://modal.com/apps/ankushmehta243/main/ap-eokiQPPBn1jbB7S6coAKUc\u001b[0m\n"
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"✓ Created objects.\n",
"├── 🔨 Created mount c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\llama.py\n",
"└── 🔨 Created function generate.\n",
"\n"
],
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"\u001b[32m✓\u001b[0m Created objects.\n",
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"\u001b[38;5;244m└── \u001b[0m🔨 Created function generate.\n"
]
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"\n"
],
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"\u001b[0m"
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"metadata": {},
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{
"data": {
"text/html": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
"\n"
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"\u001b[31mSetting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
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"data": {
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"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Stopping app - local entrypoint completed.\n",
"\n"
],
"text/plain": [
"\u001b[33mStopping app - local entrypoint completed.\n",
"\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"✓ App completed. View run at https://modal.com/apps/ankushmehta243/main/ap-eokiQPPBn1jbB7S6coAKUc\n",
"\n"
],
"text/plain": [
"\u001b[32m✓\u001b[0m App completed. \u001b[38;5;249mView run at \u001b[0m\u001b[4;38;5;249mhttps://modal.com/apps/ankushmehta243/main/ap-eokiQPPBn1jbB7S6coAKUc\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'<|begin_of_text|>Life is a mystery, everyone must stand alone, I hear you call my name,'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with modal.enable_output():\n",
" with app.run():\n",
" result=generate.remote(\"Life is a mystery, everyone must stand alone, I hear\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eaf47ba6",
"metadata": {},
"outputs": [],
"source": [
"from pricer_ephemeral import app, price"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3e3cbdc8",
"metadata": {},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
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"Output()"
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"\n"
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"\u001b[32m✓\u001b[0m Initialized. \u001b[38;5;249mView run at \u001b[0m\u001b[4;38;5;249mhttps://modal.com/apps/ankushmehta243/main/ap-zYROHiEK5Vkuc6Nxdjt8Wj\u001b[0m\n"
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"✓ Created objects.\n",
"├── 🔨 Created mount c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_ephemeral.py\n",
"└── 🔨 Created function price.\n",
"\n"
],
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"\u001b[32m✓\u001b[0m Created objects.\n",
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"\u001b[38;5;244m└── \u001b[0m🔨 Created function price.\n"
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"\u001b[32m✓\u001b[0m App completed. \u001b[38;5;249mView run at \u001b[0m\u001b[4;38;5;249mhttps://modal.com/apps/ankushmehta243/main/ap-zYROHiEK5Vkuc6Nxdjt8Wj\u001b[0m\n"
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],
"source": [
"with modal.enable_output():\n",
" with app.run():\n",
" result=price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "66706bed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"- Creating objects...\n",
"\\ Creating objects...\n",
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" c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py: Uploaded \n",
" 0/1 files\n",
"\\ Creating objects...\n",
"└── \\ Creating mount \n",
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"/ Creating objects...\n",
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"/ Creating objects...\n",
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"/ Creating objects...\n",
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"\\ Creating objects...\n",
"├── 🔨 Created mount \n",
"│ c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py\n",
"└── 🔨 Created function Pricer.*.\n",
"/ Creating objects...\n",
"├── 🔨 Created mount \n",
"│ c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py\n",
"└── 🔨 Created function Pricer.*.\n",
"\\ Creating objects...\n",
"├── 🔨 Created mount \n",
"│ c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py\n",
"└── 🔨 Created function Pricer.*.\n",
"| Creating objects...\n",
"├── 🔨 Created mount \n",
"│ c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py\n",
"└── 🔨 Created function Pricer.*.\n",
"\n",
"✓ Created objects.\n",
"├── 🔨 Created mount \n",
"│ c:\\Users\\ANKUSH\\Desktop\\price-predictor-agent\\pricer_service.py\n",
"└── 🔨 Created function Pricer.*.\n",
"✓ App deployed in 7.903s! 🎉\n",
"\n",
"View Deployment: \n",
"https://modal.com/apps/ankushmehta243/main/deployed/pricer-service\n"
]
}
],
"source": [
"!modal deploy -m pricer_service"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "170d9ff0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"133.0\n"
]
}
],
"source": [
"Pricer = modal.Cls.from_name(\"pricer-service\", \"Pricer\")\n",
"pricer = Pricer()\n",
"reply = pricer.price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n",
"print(reply)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e6101ef5",
"metadata": {},
"outputs": [],
"source": [
"from agents.specialist_agent import SpecialistAgent"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "aabd1bcc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"299.0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent = SpecialistAgent()\n",
"agent.price(\"iPad Pro 2nd generation\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cb51854",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}