Update project structure, update READMEs.

Change-Id: I72a7567c1665d1414669215b7c733451572cf1cc

README.md

@@ -1,102 +1,18 @@
-## Video Metadata Toolkit
+## **Video Metadata AI Toolkit**
 
-### Disclaimer
+### **Disclaimer**
 
 This is not an officially supported Google product. The code samples shared here
-
 are not formally supported by Google and are provided only as a reference.
 
-### Introduction
+### **Introduction**
 
-This repo currently houses two different use cases: AI Tags (generate metadata
+This repo currently houses two different use cases: AI Metadata (generate metadata
 for your videos using AI) and Smart Ad Breaks (generate cue points for your VOD
-and live video content to insert ads at the right moments).
-
-### AI Tags Development
-
-#### Setup
-
-1.  Create a Python virual environment.
-
-2.  From inside the environment run `pip install -r requirements.txt`.
-
-3.  If you plan to run anything locally, setup your Application Default
-    Credentials and project ID for GCP.
-
-4.  Make sure to run the code from within the video_metadata_toolkit directory.
-
-Application Default Credentials:
-
-```
-
-gcloud auth application-default login
-
-```
-
-Set project ID:
-
-```
-
-gcloud config set project [PROJECT_ID]
-
-```
-
-Input your GCP project ID into the project_configs.py file.
-
-#### Google Cloud
-
-You need a Google Cloud Project to each of these use cases.
-
-These are the APIs you need to enable for the AI-generated metadata tags.
-
-*   Cloud Storage
-
-*   Cloud Translation API
-
-*   Cloud Storage
-
-*   Cloud Storage API
-
-*   Cloud Speech-to-Text API
-
-*   Vertex AI API
-
-##### Run AI Tags Code
-
-[ai_metadata_generator.py](https://github.com/google-marketing-solutions/video-metadata-ai-toolkit/video-metadata-ai-toolkit/ai_metadata_generator.py) has the function which is the main entry point for
-running the code:
-
-```
-
-main(
-
-video_id,
-
-video_url,
-
-metadata="",
-
-video_title="",
-
-audio_bucket_name=project_configs.AUDIO_BUCKET_NAME,
-
-transcript_bucket_name=project_configs.TRANSCRIPT_BUCKET_NAME,
-
-)
-
-```
-
-#### Run tests
-
-From the top level project directory:
-
-```
-
-python -m unittest discover video_metadata_toolkit -p "*_test.py"
-
-```
+and live video content to insert ads at the right moments). Navigate to their respective
+folders to find more info on each of them.
 
-## Code format
+## **Code format**
 
 Submitted code should conform to Google's Python style guide. To format code
 automatically, run `pyink --pyink-indentation=2 -l 80 [FILENAME]`.
@@ -132,7 +48,7 @@ adding the following to your `settings.json`:
 
 ```
 
-#### Scripts
+#### **Scripts**
 
 To manually run tests and the linter:
 

video_metadata_toolkit/ai_metadata/README.md

@@ -0,0 +1,139 @@
+## **Video Metadata AI Toolkit**
+
+### **Disclaimer**
+
+This is not an officially supported Google product. The code samples shared here
+are not formally supported by Google and are provided only as a reference.
+
+### **Introduction**
+
+This is a Python solution which takes a publisher-provided video and generates metadata to tag the video, allowing publishers to better target ads and improve monetization. It does this by downloading the video, stripping out the audio, using Speech-to-Text to get the transcript, and sending that to Gemini to generate the metadata. Publishers can run this solution on their videos as they get uploaded to their CMS, so the metadata can be automatically added.
+
+### **AI Metadata Development**
+
+#### Setup
+
+1.  Create a Python virual environment.
+
+2.  From inside the environment run `pip install -r requirements.txt`.
+
+3.  If you plan to run anything locally, setup your Application Default
+    Credentials and project ID for GCP.
+
+
+Application Default Credentials:
+
+```
+
+gcloud auth application-default login
+
+```
+
+Set project ID:
+
+```
+
+gcloud config set project [PROJECT_ID]
+
+```
+
+Input your GCP project ID into the project_configs.py file.
+
+
+4.  Make sure to run the code from within the ai_tags directory.
+
+
+### **Google Cloud**
+
+You need a Google Cloud Project to each of these use cases.
+
+These are the APIs you need to enable for the AI-generated metadata tags.
+
+*   Cloud Storage
+
+*   Cloud Translation
+
+*   Cloud Speech-to-Text
+
+*   Vertex AI
+
+##### **Run AI Metadata Code**
+
+[ai_metadata_generator.py](https://github.com/google-marketing-solutions/video-metadata-ai-toolkit/video-metadata-ai-toolkit/ai_metadata_generator.py) has the function which is the main entry point for
+running the code:
+
+```
+
+main(
+
+video_id,
+
+video_url,
+
+metadata="",
+
+video_title="",
+
+audio_bucket_name=project_configs.AUDIO_BUCKET_NAME,
+
+transcript_bucket_name=project_configs.TRANSCRIPT_BUCKET_NAME,
+
+)
+
+```
+
+#### **Run tests**
+
+From the top level project directory:
+
+```
+
+python -m unittest discover video_metadata_toolkit -p "*_test.py"
+
+```
+
+## **Code format**
+
+Submitted code should conform to Google's Python style guide. To format code
+automatically, run `pyink --pyink-indentation=2 -l 80 [FILENAME]`.
+
+For VS Code users, you can configure the formatter to run automatically by
+adding the following to your `settings.json`:
+
+```
+
+"[python]": {
+
+"editor.defaultFormatter": "ms-python.black-formatter",
+
+"editor.formatOnSave": true,
+
+"editor.formatOnPaste": true,
+
+"editor.formatOnType": true
+
+},
+
+"black-formatter.args": [
+
+"--pyink-indentation=2"
+
+],
+
+"black-formatter.path": [
+
+"pyink"
+
+],
+
+```
+
+#### **Scripts**
+
+To manually run tests and the linter:
+
+```
+
+sh test_and_lint.sh
+
+```