Commit 3

Dockerfile

@@ -5,18 +5,22 @@ FROM python:3.9-slim
 
 WORKDIR /app
 
+# Install required packages
 COPY requirements.txt requirements.txt
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-# First copy the data file separately to ensure it exists
+# Copy data file first
 COPY data.csv .
-RUN ls -la  # Debug: List files to confirm data.csv exists
 
-# Then copy the rest of the files
+# Then copy the rest of the application files
 COPY . .
 
-# Debug: List all files
-RUN ls -la
+# Create a directory for model storage with proper permissions
+RUN mkdir -p /tmp/models && \
+    chmod 777 /tmp/models
+
+# Debug: List all files and permissions
+RUN ls -la && ls -la /tmp/models
 
 # Use Uvicorn server with appropriate logging
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--log-level", "debug"]

app.py

@@ -14,6 +14,13 @@ app = FastAPI(
     version="1.0.0"
 )
 
+# Create a models directory if it doesn't exist
+os.makedirs('/tmp/models', exist_ok=True)
+
+# Define model paths using the temporary directory
+MODEL_PATH = '/tmp/models/model.pkl'
+SCALER_PATH = '/tmp/models/scaler.pkl'
+
 # Global variables
 data = None
 model = None
@@ -27,11 +34,11 @@ features = ['Average Annual Rainfall (inches)', 'Soil Suitability (0 to 1)',
 try:
     if os.path.exists('data.csv'):
         data = pd.read_csv('data.csv')
-    if os.path.exists('model.pkl'):
-        with open('model.pkl', 'rb') as f:
+    if os.path.exists(MODEL_PATH):
+        with open(MODEL_PATH, 'rb') as f:
             model = pickle.load(f)
-    if os.path.exists('scaler.pkl'):
-        with open('scaler.pkl', 'rb') as f:
+    if os.path.exists(SCALER_PATH):
+        with open(SCALER_PATH, 'rb') as f:
             scaler = pickle.load(f)
 except Exception as e:
     print(f"Error loading data or model: {e}")
@@ -165,7 +172,7 @@ async def startup_event():
             return
     
     # Check if model and scaler need to be created
-    if not os.path.exists('model.pkl') or not os.path.exists('scaler.pkl'):
+    if not os.path.exists(MODEL_PATH) or not os.path.exists(SCALER_PATH):
         print("Training model and creating necessary files...")
         
         try:
@@ -222,11 +229,11 @@ async def startup_event():
             
             model.fit(X_train_scaled, y_train)
             
-            print("Saving model and scaler...")
-            # Save the model and scaler
-            with open('model.pkl', 'wb') as f:
+            print(f"Saving model to {MODEL_PATH} and scaler to {SCALER_PATH}...")
+            # Save the model and scaler to the temporary directory
+            with open(MODEL_PATH, 'wb') as f:
                 pickle.dump(model, f)
-            with open('scaler.pkl', 'wb') as f:
+            with open(SCALER_PATH, 'wb') as f:
                 pickle.dump(scaler, f)
             
             print("Model and scaler saved successfully!")
@@ -239,17 +246,17 @@ async def startup_event():
         # Load model and scaler if they exist but weren't loaded
         if model is None:
             try:
-                with open('model.pkl', 'rb') as f:
+                with open(MODEL_PATH, 'rb') as f:
                     model = pickle.load(f)
-                print("Successfully loaded existing model")
+                print(f"Successfully loaded existing model from {MODEL_PATH}")
             except Exception as e:
                 print(f"Failed to load model: {e}")
                 
         if scaler is None:
             try:
-                with open('scaler.pkl', 'rb') as f:
+                with open(SCALER_PATH, 'rb') as f:
                     scaler = pickle.load(f)
-                print("Successfully loaded existing scaler")
+                print(f"Successfully loaded existing scaler from {SCALER_PATH}")
             except Exception as e:
                 print(f"Failed to load scaler: {e}")