hf issues now fixed

Dockerfile

@@ -17,9 +17,9 @@ ENV PYTHONPATH="/app"
 
 # Health check
 HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
-    CMD curl -f http://localhost:8000/docs || exit 1
+    CMD curl -f http://localhost:7860/health || exit 1
 
-EXPOSE 8000
+EXPOSE 7860
 
 # Run server with updated root OOP Path
-CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,12 +0,0 @@
----
-title: Sql Agent
-emoji: 😻
-colorFrom: green
-colorTo: yellow
-sdk: docker
-pinned: false
-license: apache-2.0
-short_description: sql-agent
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

openenv.yaml

@@ -3,4 +3,4 @@ name: sql-agent-env
 type: space
 runtime: fastapi
 app: server.app:app
-port: 8000
+port: 7860

readme.md

@@ -1,71 +0,0 @@
----
-title: SQL AI Data Analyst
-emoji: 📊
-colorFrom: blue
-colorTo: green
-sdk: docker
-app_port: 8000
-tags:
-  - openenv
----
-# OpenEnv SQL Data Analyst
-
-## The Goal
-The objective is straightforward: the AI must interpret an internal database schema and translate human business instructions into valid SQL queries natively executed against the pipeline. 
-
-Tasks increase in difficulty, pushing models to understand complex relationships such as:
-- Simple Table Retrievals (`SELECT`, `WHERE`)
-- Aggregating related tables (`JOIN`, `GROUP BY`)
-- Complex constraint execution bounds (Logic matching)
-
-## Fast Execution
-1. **Install:** `pip install -r requirements.txt`
-2. **Setup your preferred engine:**
-   ```powershell
-   $env:GEMINI_API_KEY="your-api-key" 
-   # Or use OPENAI_API_KEY 
-   ```
-3. **Run the Simulation Base:**
-   ```powershell
-   python inference.py
-   ```
-
-## Architecture Workflow
-
-This environment follows strict [OpenEnv Gym interface](https://github.com/meta-pytorch/OpenEnv) standards, utilizing `step()`, `reset()`, and `state()` endpoints wrapped locally or exposed natively via Hugging Face.
-
-```mermaid
-flowchart TD
-    Start([Episode Start]) --> EnvInit[Environment loads Task 1]
-    
-    EnvInit --> Obs[Generate Observation]
-    Obs -.-> |1. Send Schema and Instruction| Agent((LLM Agent))
-    
-    Agent -.-> |2. Predicts SQL Query| Step[Environment step function]
-    
-    Step --> Exec{Execute SQL on SQLite Engine}
-    
-    Exec -->|SQL Syntax Error| Pen[Return Negative Reward]
-    Exec -->|Valid SQL| Grader[Evaluate Pandas DataFrame]
-    
-    Grader --> Match{Compare Expected DataFrame}
-    Match -->|Perfect Match| Rew1[Reward 1.0 Advance Level]
-    Match -->|Unordered Match| RewPartial[Reward 0.8]
-    Match -->|Incorrect Data| Rew0[Reward 0.0]
-    
-    Pen --> StateUpdate
-    Rew1 --> StateUpdate
-    RewPartial --> StateUpdate
-    Rew0 --> StateUpdate
-    
-    StateUpdate[Log History and Update Episode state] --> CheckDone{All Tasks Completed Yes or No}
-    
-    CheckDone -->|No| Obs
-    CheckDone -->|Yes| Finish([Episode End])
-```
-
-## Internal Engine Design
-To read more about exactly how the logic is handled under the hood, access the OOP components inside the root directory:
-- `server/environment.py`: The orchestrator handling the OpenEnv lifecycle.
-- `database/sqlite_manager.py`: Creates ephemeral SQLite sandboxes per session.
-- `core/grader.py`: Evaluates logic correctness independent of string semantics by leveraging Pandas DataFrame alignments.

server/app.py

@@ -4,3 +4,13 @@ from models import SqlAction, SqlObservation
 from server.environment import SqlEnvironment
 
 app = create_fastapi_app(SqlEnvironment, SqlAction, SqlObservation)
+
+
+@app.get("/")
+def root():
+    return {"status": "ok"}
+
+
+@app.get("/health")
+def health():
+    return {"status": "healthy"}