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Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# Create data directory with permissions
+RUN mkdir -p /app/data && chmod 777 /app/data
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,5 +1,17 @@
----
-license: other
-license_name: cn
-license_link: LICENSE
----
+---
+title: CE Data Collector
+emoji: 📊
+colorFrom: green
+colorTo: lime
+sdk: docker
+pinned: false
+app_port: 7860
+---
+
+# Creative Engine Data Collector
+
+This API receives anonymous CES code snippets and metadata from the Creative Engine editor for training purposes.
+
+## API Endpoint
+
+- `POST /collect`: Accepts a JSON object with `scripts` and `metadata`.

app.py

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+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+import json
+import os
+import time
+from datetime import datetime
+
+app = FastAPI()
+
+# Enable CORS for the editor
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Directory to store collected datasets (simulating HF Dataset storage)
+DATA_DIR = "/app/data"
+os.makedirs(DATA_DIR, exist_ok=True)
+
+@app.get("/")
+async def root():
+    return {"message": "Creative Engine Data Collector is running"}
+
+@app.post("/collect")
+async def collect_data(request: Request):
+    try:
+        data = await request.json()
+
+        # We expect: { "scripts": [...], "metadata": { "engineVersion": "0.1.2", ... } }
+        timestamp = int(time.time())
+        filename = f"ces_collection_{timestamp}.json"
+        filepath = os.path.join(DATA_DIR, filename)
+
+        # Save to local disk
+        with open(filepath, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+
+        # Optional: Push to Hugging Face Hub if token is present
+        hf_token = os.environ.get("HF_TOKEN")
+        dataset_repo = "Carley1234/DatosdeentrenamientodeCreativeCode"
+
+        if hf_token:
+            try:
+                from huggingface_hub import HfApi
+                api = HfApi()
+                api.upload_file(
+                    path_or_fileobj=filepath,
+                    path_in_repo=f"data/{filename}",
+                    repo_id=dataset_repo,
+                    repo_type="dataset",
+                    token=hf_token
+                )
+                print(f"Successfully pushed {filename} to {dataset_repo}")
+            except Exception as hf_err:
+                print(f"Failed to push to HF Hub: {hf_err}")
+
+        print(f"Collected data from engine version {data.get('metadata', {}).get('engineVersion')}")
+
+        return {"status": "success", "file": filename}
+    except Exception as e:
+        print(f"Error collecting data: {e}")
+        return {"status": "error", "message": str(e)}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+huggingface_hub