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This commit introduces two new generative features to the AI agent:
1. **AI-Powered Backend Generation:**
- A `generate_backend` function in `app.py` creates a simple Python Flask backend from a prompt.
- A new API endpoint, `/api/v1/develop/backend`, exposes this functionality.
2. **AI/ML Model Generation:**
- A `generate_ml_model` function in `app.py` creates boilerplate `scikit-learn` model code.
- A new API endpoint, `/api/v1/ml/model`, exposes this functionality.
The frontend (`index.html`) has been updated to include service cards for both new features, making them visible to users.
This commit introduces three major new features to the AI agent, significantly expanding its capabilities:
1. **AI Orchestrator:**
- Implements an `orchestrate_task` function that can parse high-level user goals and call other generative tools to achieve them.
- Exposed via a new `/api/v1/orchestrate` endpoint.
- This feature addresses the user's request for "AGI capacities" by providing a more general, multi-step problem-solving capability.
2. **AI-Powered Backend Generation:**
- Adds a `generate_backend` function to create simple Python Flask backends from a prompt.
- Exposed via a new `/api/v1/develop/backend` endpoint.
3. **AI/ML Model Generation:**
- Adds a `generate_ml_model` function to create boilerplate `scikit-learn` model code for classification or regression tasks.
- Exposed via a new `/api/v1/ml/model` endpoint.
All new features are advertised on the frontend with corresponding service cards in `index.html`.
Reviewer's GuideThis PR introduces backend and ML model generation functions, an orchestration mechanism, corresponding API endpoints, and frontend updates to showcase new AI services. Sequence diagram for orchestrate API endpointsequenceDiagram
participant User as actor User
participant Frontend
participant Backend
participant AI as AI Logic
User->>Frontend: Sends request to /api/v1/orchestrate with prompt
Frontend->>Backend: POST /api/v1/orchestrate
Backend->>AI: orchestrate_task(prompt)
AI->>AI: Calls appropriate generation functions (e.g., generate_backend)
AI-->>Backend: Returns generated content
Backend-->>Frontend: Responds with generated message
Frontend-->>User: Displays result
Sequence diagram for backend generation API endpointsequenceDiagram
participant User as actor User
participant Frontend
participant Backend
participant AI as AI Logic
User->>Frontend: Sends request to /api/v1/develop/backend with prompt
Frontend->>Backend: POST /api/v1/develop/backend
Backend->>AI: generate_backend(prompt)
AI-->>Backend: Returns backend code
Backend-->>Frontend: Responds with generated code
Frontend-->>User: Displays backend code
Sequence diagram for ML model generation API endpointsequenceDiagram
participant User as actor User
participant Frontend
participant Backend
participant AI as AI Logic
User->>Frontend: Sends request to /api/v1/ml/model with prompt
Frontend->>Backend: POST /api/v1/ml/model
Backend->>AI: generate_ml_model(prompt)
AI-->>Backend: Returns ML model code
Backend-->>Frontend: Responds with generated code
Frontend-->>User: Displays ML model code
Class diagram for new backend and ML model generation functionsclassDiagram
class App
class AI_Logic {
+generate_backend(prompt)
+generate_ml_model(prompt)
+orchestrate_task(prompt)
}
class FlaskApp {
+orchestrate_endpoint()
+develop_backend_endpoint()
+ml_model_endpoint()
}
App <|-- FlaskApp
FlaskApp o-- AI_Logic
File-Level Changes
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Hey there - I've reviewed your changes and found some issues that need to be addressed.
- Refactor the repeated prompt parsing loops in generate_backend and generate_ml_model into a shared utility to avoid duplicated code.
- Since you’re embedding prompt‐derived strings directly into generated code templates, add input sanitization or escaping to avoid unexpected syntax errors or injection issues.
- The simple substring checks in orchestrate_task may misclassify user intents—consider a more robust command or intent parsing approach rather than basic keyword matching.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- Refactor the repeated prompt parsing loops in generate_backend and generate_ml_model into a shared utility to avoid duplicated code.
- Since you’re embedding prompt‐derived strings directly into generated code templates, add input sanitization or escaping to avoid unexpected syntax errors or injection issues.
- The simple substring checks in orchestrate_task may misclassify user intents—consider a more robust command or intent parsing approach rather than basic keyword matching.
## Individual Comments
### Comment 1
<location> `app.py:422` </location>
<code_context>
+def generate_ml_model(prompt):
</code_context>
<issue_to_address>
ML model code generation assumes classification or regression only and does not validate model type or library.
Add checks to ensure only supported model types and libraries are accepted, and handle unsupported cases gracefully.
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| @@ -375,6 +375,38 @@ def generate_website(prompt): | |||
| """ | |||
| return response_message.strip() | |||
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suggestion (bug_risk): ML model code generation assumes classification or regression only and does not validate model type or library.
Add checks to ensure only supported model types and libraries are accepted, and handle unsupported cases gracefully.
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Summary by Sourcery
Add backend and ML model code generation capabilities, a task orchestrator, corresponding API endpoints, and update the frontend to showcase new services.
New Features: