Update app.py

app.py

@@ -0,0 +1,155 @@
+"""
+MimiReady Python Executor Backend
+Exécute du code Python avec TensorFlow/Keras sur Hugging Face Spaces
+"""
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Optional
+import sys
+import io
+import base64
+import traceback
+from contextlib import redirect_stdout, redirect_stderr
+
+# Configuration matplotlib avant import
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+app = FastAPI(title="MimiReady Python Executor")
+
+# CORS pour permettre les requêtes depuis le frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class CodeRequest(BaseModel):
+    code: str
+    timeout: int = 30
+
+class CodeResponse(BaseModel):
+    stdout: str
+    stderr: str
+    plots: List[str]
+    success: bool
+    error: Optional[str] = None
+
+# Variables globales pour capturer les plots
+_plot_images: List[str] = []
+
+def capture_plots():
+    """Capture tous les plots matplotlib en base64"""
+    global _plot_images
+    _plot_images = []
+    
+    original_show = plt.show
+    
+    def patched_show(*args, **kwargs):
+        global _plot_images
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight', facecolor='white')
+        buf.seek(0)
+        img_str = base64.b64encode(buf.read()).decode('utf-8')
+        _plot_images.append(img_str)
+        plt.clf()
+        plt.close('all')
+    
+    plt.show = patched_show
+    return original_show
+
+def restore_show(original_show):
+    """Restaure plt.show original"""
+    plt.show = original_show
+
+@app.get("/")
+def read_root():
+    return {
+        "service": "MimiReady Python Executor",
+        "status": "running",
+        "capabilities": ["numpy", "pandas", "matplotlib", "scikit-learn", "tensorflow", "keras"]
+    }
+
+@app.get("/health")
+def health_check():
+    return {"status": "healthy"}
+
+@app.post("/execute", response_model=CodeResponse)
+async def execute_code(request: CodeRequest):
+    """Exécute du code Python et retourne les résultats"""
+    global _plot_images
+    
+    stdout_capture = io.StringIO()
+    stderr_capture = io.StringIO()
+    
+    # Préparer l'environnement d'exécution
+    exec_globals = {
+        "__builtins__": __builtins__,
+        "__name__": "__main__",
+    }
+    
+    # Pre-import des bibliothèques courantes
+    try:
+        import numpy as np
+        exec_globals["np"] = np
+        exec_globals["numpy"] = np
+    except ImportError:
+        pass
+    
+    try:
+        import pandas as pd
+        exec_globals["pd"] = pd
+        exec_globals["pandas"] = pd
+    except ImportError:
+        pass
+    
+    try:
+        import sklearn
+        exec_globals["sklearn"] = sklearn
+    except ImportError:
+        pass
+    
+    # TensorFlow/Keras
+    try:
+        import tensorflow as tf
+        from tensorflow import keras
+        exec_globals["tf"] = tf
+        exec_globals["tensorflow"] = tf
+        exec_globals["keras"] = keras
+    except ImportError as e:
+        print(f"TensorFlow import warning: {e}")
+    
+    # Matplotlib
+    exec_globals["plt"] = plt
+    exec_globals["matplotlib"] = matplotlib
+    
+    success = True
+    error_msg = None
+    
+    original_show = capture_plots()
+    
+    try:
+        with redirect_stdout(stdout_capture), redirect_stderr(stderr_capture):
+            exec(request.code, exec_globals)
+    except Exception as e:
+        success = False
+        error_msg = f"{type(e).__name__}: {str(e)}\n{traceback.format_exc()}"
+    finally:
+        restore_show(original_show)
+    
+    return CodeResponse(
+        stdout=stdout_capture.getvalue(),
+        stderr=stderr_capture.getvalue(),
+        plots=_plot_images.copy(),
+        success=success,
+        error=error_msg
+    )
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)