fixes webapp, updates text (#867)

09_job_queues/doc_ocr_jobs.py

@@ -6,22 +6,22 @@
 #
 # This tutorial shows you how to use Modal as an infinitely scalable job queue
 # that can service async tasks from a web app. For the purpose of this tutorial,
-# we've also built a [React + FastAPI web app on Modal](/docs/examples/doc_ocr_webapp)
+# we've also built a [React + FastAPI web app on Modal](https://modal.com/docs/examples/doc_ocr_webapp)
 # that works together with it, but note that you don't need a web app running on Modal
 # to use this pattern. You can submit async tasks to Modal from any Python
 # application (for example, a regular Django app running on Kubernetes).
 #
 # Our job queue will handle a single task: running OCR transcription for images.
 # We'll make use of a pre-trained Document Understanding model using the
-# [donut](https://github.com/clovaai/donut) package to accomplish this. Try
-# it out for yourself [here](https://modal-labs-example-doc-ocr-webapp-wrapper.modal.run/).
+# [`donut`](https://github.com/clovaai/donut) package. Try
+# it out for yourself [here](https://modal-labs--example-doc-ocr-webapp-wrapper.modal.run/).
 #
 # ![receipt parser frontend](./receipt_parser_frontend_2.jpg)
 
 # ## Define an App
 #
-# Let's first import `modal` and define a [`App`](/docs/reference/modal.App). Later, we'll use the name provided
-# for our `App` to find it from our web app, and submit tasks to it.
+# Let's first import `modal` and define a [`App`](https://modal.com/docs/reference/modal.App).
+# Later, we'll use the name provided for our `App` to find it from our web app, and submit tasks to it.
 
 import urllib.request
 
@@ -33,17 +33,22 @@
 #
 # `donut` downloads the weights for pre-trained models to a local directory, if those weights don't already exist.
 # To decrease start-up time, we want this download to happen just once, even across separate function invocations.
-# To accomplish this, we use the [`Image.run_function`](/docs/reference/modal.Image#run_function) method, which allows
-# us to run some code at image build time to save the model weights into the image.
+# To accomplish this, we use the [`Image.run_function`](https://modal.com/docs/reference/modal.Image#run_function) method,
+# which allows us to run some code at image build time to save the model weights into the image.
 
 CACHE_PATH = "/root/model_cache"
 MODEL_NAME = "naver-clova-ix/donut-base-finetuned-cord-v2"
 
 
-def download_model_weights() -> None:
-    from huggingface_hub import snapshot_download
+def get_model():
+    from donut import DonutModel
+
+    pretrained_model = DonutModel.from_pretrained(
+        MODEL_NAME,
+        cache_dir=CACHE_PATH,
+    )
 
-    snapshot_download(repo_id=MODEL_NAME, cache_dir=CACHE_PATH)
+    return pretrained_model
 
 
 image = (
@@ -54,15 +59,15 @@ def download_model_weights() -> None:
         "transformers==4.21.3",
         "timm==0.5.4",
     )
-    .run_function(download_model_weights)
+    .run_function(get_model)
 )
 
 # ## Handler function
 #
 # Now let's define our handler function. Using the [@app.function()](https://modal.com/docs/reference/modal.App#function)
-# decorator, we set up a Modal [Function](/docs/reference/modal.Function) that uses GPUs,
-# runs on a [custom container image](/docs/guide/custom-container),
-# and automatically [retries](/docs/guide/retries#function-retries) failures up to 3 times.
+# decorator, we set up a Modal [Function](https://modal.com/docs/reference/modal.Function) that uses GPUs,
+# runs on a [custom container image](https://modal.com/docs/guide/custom-container),
+# and automatically [retries](https://modal.com/docs/guide/retries#function-retries) failures up to 3 times.
 
 
 @app.function(
@@ -74,15 +79,11 @@ def parse_receipt(image: bytes):
     import io
 
     import torch
-    from donut import DonutModel
     from PIL import Image
 
     # Use donut fine-tuned on an OCR dataset.
     task_prompt = "<s_cord-v2>"
-    pretrained_model = DonutModel.from_pretrained(
-        MODEL_NAME,
-        cache_dir=CACHE_PATH,
-    )
+    pretrained_model = get_model()
 
     # Initialize model.
     pretrained_model.half()
@@ -107,8 +108,8 @@ def parse_receipt(image: bytes):
 # modal deploy doc_ocr_jobs.py
 # ```
 #
-# Once it's published, we can [look up](/docs/guide/trigger-deployed-functions) this function from another
-# Python process and submit tasks to it:
+# Once it's published, we can [look up](https://modal.com/docs/guide/trigger-deployed-functions) this function
+# from another Python process and submit tasks to it:
 #
 # ```python
 # fn = modal.Function.lookup("example-doc-ocr-jobs", "parse_receipt")
@@ -117,7 +118,7 @@ def parse_receipt(image: bytes):
 #
 # Modal will auto-scale to handle all the tasks queued, and
 # then scale back down to 0 when there's no work left. To see how you could use this from a Python web
-# app, take a look at the [receipt parser frontend](/docs/examples/doc_ocr_webapp)
+# app, take a look at the [receipt parser frontend](https://modal.com/docs/examples/doc_ocr_webapp)
 # tutorial.
 
 # ## Run manually
@@ -136,8 +137,9 @@ def main():
     if receipt_filename.exists():
         with open(receipt_filename, "rb") as f:
             image = f.read()
+        print(f"running OCR on {f.name}")
     else:
-        image = urllib.request.urlopen(
-            "https://nwlc.org/wp-content/uploads/2022/01/Brandys-walmart-receipt-8.webp"
-        ).read()
+        receipt_url = "https://nwlc.org/wp-content/uploads/2022/01/Brandys-walmart-receipt-8.webp"
+        image = urllib.request.urlopen(receipt_url).read()
+        print(f"running OCR on sample from URL {receipt_url}")
     print(parse_receipt.remote(image))

09_job_queues/doc_ocr_webapp.py

@@ -1,23 +1,23 @@
 # ---
 # deploy: true
-# lambda-test: false
+# cmd: ["modal", "serve", "09_job_queues/doc_ocr_webapp.py"]
 # ---
 #
 # # Document OCR web app
 #
 # This tutorial shows you how to use Modal to deploy a fully serverless
 # [React](https://reactjs.org/) + [FastAPI](https://fastapi.tiangolo.com/) application.
 # We're going to build a simple "Receipt Parser" web app that submits OCR transcription
-# tasks to a separate Modal app defined in the [Job Queue
-# tutorial](/docs/examples/doc_ocr_jobs), polls until the task is completed, and displays
+# tasks to a separate Modal app defined in the [Job Queue tutorial](https://modal.com/docs/examples/doc_ocr_jobs),
+# polls until the task is completed, and displays
 # the results. Try it out for yourself
 # [here](https://modal-labs--example-doc-ocr-webapp-wrapper.modal.run/).
 #
 # ![receipt parser frontend](./receipt_parser_frontend.jpg)
 
 # ## Basic setup
 #
-# Let's get the imports out of the way and define a [`App`](/docs/reference/modal.App).
+# Let's get the imports out of the way and define a [`App`](https://modal.com/docs/reference/modal.App).
 
 from pathlib import Path
 
@@ -27,8 +27,8 @@
 
 app = modal.App("example-doc-ocr-webapp")
 
-# Modal works with any [ASGI](/docs/guide/webhooks#serving-asgi-and-wsgi-apps) or
-# [WSGI](/docs/guide/webhooks#wsgi) web framework. Here, we choose to use [FastAPI](https://fastapi.tiangolo.com/).
+# Modal works with any [ASGI](https://modal.com/docs/guide/webhooks#serving-asgi-and-wsgi-apps) or
+# [WSGI](https://modal.com/docs/guide/webhooks#wsgi) web framework. Here, we choose to use [FastAPI](https://fastapi.tiangolo.com/).
 
 web_app = fastapi.FastAPI()
 
@@ -38,12 +38,13 @@
 # and another to poll for the results of the job.
 #
 # In `parse`, we're going to submit tasks to the function defined in the [Job
-# Queue tutorial](/docs/examples/doc_ocr_jobs), so we import it first using
-# [`Function.lookup`](/docs/reference/modal.Function#lookup).
+# Queue tutorial](https://modal.com/docs/examples/doc_ocr_jobs), so we import it first using
+# [`Function.lookup`](https://modal.com/docs/reference/modal.Function#lookup).
 #
-# We call [`.spawn()`](/docs/reference/modal.Function#spawn) on the function handle
-# we imported above, to kick off our function without blocking on the results. `spawn` returns
-# a unique ID for the function call, that we can use later to poll for its result.
+# We call [`.spawn()`](https://modal.com/docs/reference/modal.Function#spawn) on the function handle
+# we imported above to kick off our function without blocking on the results. `spawn` returns
+# a unique ID for the function call, which we then use
+# to poll for its result.
 
 
 @web_app.post("/parse")
@@ -65,7 +66,7 @@ async def parse(request: fastapi.Request):
 
 @web_app.get("/result/{call_id}")
 async def poll_results(call_id: str):
-    function_call = modal.FunctionCall.from_id(call_id)
+    function_call = modal.functions.FunctionCall.from_id(call_id)
     try:
         result = function_call.get(timeout=0)
     except TimeoutError:
@@ -77,7 +78,7 @@ async def poll_results(call_id: str):
 # Finally, we mount the static files for our front-end. We've made [a simple React
 # app](https://github.com/modal-labs/modal-examples/tree/main/09_job_queues/doc_ocr_frontend)
 # that hits the two endpoints defined above. To package these files with our app, first
-# we get the local assets path, and then create a modal [`Mount`](/docs/guide/local-data#mounting-directories)
+# we get the local assets path, and then create a modal [`Mount`](https://modal.com/docs/guide/local-data#mounting-directories)
 # that mounts this directory at `/assets` inside our container. Then, we instruct FastAPI to [serve
 # this static file directory](https://fastapi.tiangolo.com/tutorial/static-files/) at our root path.
 
@@ -98,27 +99,27 @@ def wrapper():
 
 # ## Running
 #
-# You can run this as an ephemeral app, by running the command
+# While developing, you can run this as an ephemeral app by executing the command
 #
 # ```shell
 # modal serve doc_ocr_webapp.py
 # ```
 #
+# Modal watches all the mounted files and updates the app if anything changes.
+# See [these docs](https://www.notion.so/modal-com/Simple-RAG-chat-with-PDF-app-a20fc171f0fc415cb320606f737e6db4?pvs=4)
+# for more details.
+#
 # ## Deploy
 #
-# That's all! To deploy your application, run
+# To deploy your application, run
 #
 # ```shell
 # modal deploy doc_ocr_webapp.py
 # ```
 #
-# If successful, this will print a URL for your app, that you can navigate to from
+# That's all!
+#
+# If successful, this will print a URL for your app that you can navigate to in
 # your browser 🎉 .
 #
 # ![receipt parser processed](./receipt_parser_frontend_2.jpg)
-#
-# ## Developing
-#
-# If desired, instead of deploying, we can [serve](/docs/guide/webhooks#developing-with-modal-serve)
-# our app ephemerally. In this case, Modal watches all the mounted files, and updates
-# the app if anything changes.