Flatten structure: Move all files from app/ to root directory

Dockerfile

@@ -23,5 +23,6 @@ EXPOSE 7860
 ENV PYTHONPATH=/code
 ENV TMPDIR=/tmp
 
+# Command to run the application with explicit path
 # Command to run the application
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

app/main.py → main.py

@@ -1,4 +1,4 @@
-# app/main.py
+# main.py
 
 import shutil
 import os
@@ -6,11 +6,11 @@ import uuid
 from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
-from app.predict import predict_image
+from predict import predict_image
 
 app = FastAPI(
-    title="GP-Tea Skin Analysis API",
-    description="AI-powered skin condition analysis service",
+    title="Medical Image Classification API",
+    description="AI-powered medical image classification service",
     version="1.0.0"
 )
 
@@ -23,8 +23,10 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Create uploads directory
-os.makedirs("app/uploads", exist_ok=True)
+# Create uploads directory in tmp (writable in containers)
+import tempfile
+UPLOAD_DIR = tempfile.mkdtemp()
+os.makedirs(UPLOAD_DIR, exist_ok=True)
 
 @app.get("/health")
 async def health_check():
@@ -38,7 +40,7 @@ async def analyze_image(file: UploadFile = File(...)):
             raise HTTPException(status_code=400, detail="File must be an image")
         
         unique_filename = f"{uuid.uuid4().hex}_{file.filename}"
-        file_path = f"app/uploads/{unique_filename}"
+        file_path = os.path.join(UPLOAD_DIR, unique_filename)
         
         with open(file_path, "wb") as buffer:
             shutil.copyfileobj(file.file, buffer)

app/model_loader.py → model_loader.py

@@ -1,9 +1,9 @@
-# app/model_loader.py
+# model_loader.py
 import os
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
 
-MODEL_PATH = "app/model/efficientnetv2s.h5"
+MODEL_PATH = "model/efficientnetv2s.h5"
 REPO_ID = "Miguel764/efficientnetv2s-skin-cancer-classifier"
 FILENAME = "efficientnetv2s.h5"
 
@@ -14,7 +14,7 @@ def load_model():
         hf_hub_download(
             repo_id=REPO_ID,
             filename=FILENAME,
-            local_dir="app/model"
+            local_dir="model"
         )
     else:
         print("Model already exists locally.")

app/predict.py → predict.py

@@ -1,8 +1,8 @@
-# app/predict.py
+# predict.py
 from tensorflow.keras.preprocessing import image
 import numpy as np
 import tensorflow as tf
-from app.model_loader import load_model
+from model_loader import load_model
 
 model = load_model()