chore: fixing readme and error handler for json parsing

GoogleCloudPlatform · Sep 26, 2024 · 7cb37c2 · 7cb37c2
1 parent 00c5e11
commit 7cb37c2
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Showing 2 changed files with 32 additions and 10 deletions.
diff --git a/infrastructure/cymbal-store-embeddings/README.md b/infrastructure/cymbal-store-embeddings/README.md
@@ -90,21 +90,38 @@ gunicorn --bind :8080 --reload --workers 1 --threads 8 --timeout 0 cymbal_store:
 ```
 
 ### Deploy the applicaion to Cloud Run
+Create a service account cymbal-store-identity and grant role VertexAI User to the account - optional now since we are not using Vertex AI as of now.
+Build and deploy application to the Cloud Run service.
+
 ```
 gcloud alpha run deploy cymbal-store \
    --source=./ \
    --no-allow-unauthenticated \
    --service-account cymbal-store-identity \
    --region us-central1 \
    --network=default \
+   --set-env-vars=DB_USER=cymbaldb_owner,DB_PASS=StrongPassword,DB_NAME=cymbaldb,INSTANCE_HOST=127.0.0.1,DB_PORT=5432 \
    --quiet
 ```
-
-* Requests to Try 
+### Use the application
+#### Choose the model
+- Click "Model" on the bottom of the application and new dialog window will be opened
+- Provide your Google AI API token 
+- Switch focus to models and click checkbox for Gemeini 1.5 flash model
+- Click "Confirm"
+
+#### Ask questions
+- Ask questions in the chat
+- Please report all the issues with the application 
+- Requests to Try 
   - Choose model using button in the chat and providing your Google AI token and choosing the Gemini model (Open AI is not supported yet)
   - Confirm the choice of the model
   - Ask in the chat - "What kind of fruit trees grow well here?"
 
+# TO DO
+- Add support for other models and providers
+- Add error handlers when AI provider returns an error
+
 # License
 Apache License Version 2.0; 
 Copyright 2024 Google LLC

diff --git a/infrastructure/cymbal-store-embeddings/cymbal_store.py b/infrastructure/cymbal-store-embeddings/cymbal_store.py
@@ -335,6 +335,7 @@ def on_input_enter(e: me.InputEnterEvent):
         yield from send_prompt(e)
 
 
+
     with me.box(style=_STYLE_APP_CONTAINER):
         with me.content_button(
             type="icon",
@@ -419,6 +420,7 @@ def send_prompt(e: me.ClickEvent):
             state.conversations.append(Conversation(model=model, messages=[]))
     input = state.input
     state.input = ""
+    yield
 
     for conversation in state.conversations:
         model = conversation.model
@@ -429,14 +431,17 @@ def send_prompt(e: me.ClickEvent):
         yield
 
         if model == Models.GEMINI_1_5_FLASH.value:
-            intent_str = gemini_model.classify_intent(input)
-            print(intent_str)
-            logging.info(f"PRODUCTS LIST: {intent_str}")
-            try:
-                json_intent = json.loads(intent_str)
-            except json.JSONDecodeError as e:
-                print(f"Error decoding JSON: {e}")
-            json_intent = json.loads(intent_str)
+            while True:
+                intent_str = gemini_model.classify_intent(input)
+                print(intent_str)
+                logging.info(f"PRODUCTS LIST: {intent_str}")
+                try:
+                    json_intent = json.loads(intent_str)
+                except json.JSONDecodeError as e:
+                    print(f"Error decoding JSON: {e}")
+                    continue
+                break
+
             if json_intent["shouldRecommendProduct"] is True:
                 search_embedding = gemini_model.generate_embedding(json_intent["summary"])
                 products_list = get_products(db, str(search_embedding["embedding"]))