Added Code files

.gitignore

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+# Python bytecode and cache
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Environments
+.venv/
+venv/
+ENV/
+env/
+bin/
+lib/
+include/
+
+# AI Models & Large Files
+# (Usually better to keep models on Google Drive/OneDrive, not Git)
+*.h5
+*.keras
+*.pkl
+*.pt
+*.pth
+*.weights
+
+# Logs and databases
+*.log
+api.log
+*.db
+*.sqlite3
+
+# OS related files
+.DS_Store
+Thumbs.db
+
+# Environment variables (Crucial for security)
+.env
+.flaskenv
+
+# Editor/IDE specific
+.vscode/
+.idea/

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies for Pillow
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Port 7860 is the default for Hugging Face
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

best_fruit_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e5a62e15c7b6881e13a557e8e740e6e922a8be6f03e145bfc07a055009342a8
+size 40997712

main.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from fastapi.responses import JSONResponse
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import io
+import os
+import slowapi
+from slowapi import Limiter, _rate_limit_exceeded_handler
+from slowapi.util import get_remote_address
+from slowapi.errors import RateLimitExceeded
+import logging 
+
+
+logging.basicConfig(
+    filename='api.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+app = FastAPI(
+    title="Cloud Inventory AI API",
+    description="API for scanning fruits and returning the Fruit Name and Quality.",
+    root_path="/api"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],        
+    allow_credentials=False,     
+    allow_methods=["GET", "POST"],           
+    allow_headers=["*"],           
+)
+
+# Use get_remote_address which is safer and handles proxies better than x.client.ip
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+
+# Register the exception handler so rate-limited users get a proper HTTP 429 response
+app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
+
+class CastLayer(tf.keras.layers.Layer):
+    def call(self, inputs):
+        return tf.cast(inputs, tf.float32)
+
+MODEL_PATH = "best_fruit_model.h5" 
+model = None
+
+# 3. Replace print() with logging
+if os.path.exists(MODEL_PATH):
+    try:
+        custom_objects = {'Cast': CastLayer}
+        model = tf.keras.models.load_model(MODEL_PATH, custom_objects=custom_objects, compile=False)
+        logging.info("Model loaded successfully!") 
+    except Exception as e:
+        logging.error(f"Error loading model: {e}")
+else:
+    logging.warning(f"Warning: Model not found at {MODEL_PATH}")
+
+# Your 20-class list
+CLASS_NAMES = [
+    'fresh_apple', 'fresh_banana', 'fresh_cucumber', 'fresh_grape', 
+    'fresh_guava', 'fresh_mango', 'fresh_orange', 'fresh_pomegranate', 
+    'fresh_strawberry', 'fresh_tomato', 'rotten_apple', 'rotten_banana', 
+    'rotten_cucumber', 'rotten_grape', 'rotten_guava', 'rotten_mango', 
+    'rotten_orange', 'rotten_pomegranate', 'rotten_strawberry', 'rotten_tomato'
+]
+
+@app.get("/")
+@limiter.limit("40/minute")
+async def root_call(request: Request):
+    logging.info(f"Root endpoint accessed by {request.client.host}")
+    return {"message": "Fruit Quality API is running. Go to /docs to test it."}
+
+@app.get("/health")
+@limiter.limit("40/minute")
+async def health_call(request: Request):
+    if model is None:
+        logging.warning("Health check failed: Model not loaded.")
+        return {"status": "unhealthy", "reason": "Model missing or failed to load."}
+    return {"status": "healthy", "model_loaded": True}
+
+@app.post("/predict")
+@limiter.limit("40/minute")
+async def predict_image(request: Request, file: UploadFile = File(...)):
+    logging.info(f"Prediction request received from {request.client.host} for file {file.filename}")
+    
+    if model is None:
+        logging.error("Prediction attempted, but model is not loaded.")
+        raise HTTPException(status_code=503, detail="Model is not loaded.")
+    
+    if not file.content_type.startswith("image/"):
+        logging.warning(f"Invalid file type uploaded: {file.content_type}")
+        raise HTTPException(status_code=400, detail="Invalid file. Upload an image.")
+
+    try:
+        contents = await file.read()
+        img = Image.open(io.BytesIO(contents)).convert('RGB')
+        
+        img = img.resize((224, 224))
+        img_arr = np.array(img) / 255.0  
+        img_arr = np.expand_dims(img_arr, axis=0) 
+        
+        preds = model.predict(img_arr, verbose=0)
+        idx = int(np.argmax(preds[0])) 
+        raw_label = CLASS_NAMES[idx]
+        
+        parts = raw_label.split('_', 1) 
+        
+        quality = parts[0].capitalize() 
+        fruit_name = parts[1].title()   
+
+        logging.info(f"Prediction successful: {quality} {fruit_name}")
+
+        return JSONResponse(content={
+            "fruit": fruit_name,
+            "quality": quality
+        })
+
+    except Exception as e:
+        logging.error(f"Server error during prediction: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Server error: {str(e)}")

requirements.txt

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+fastapi
+uvicorn
+tensorflow-cpu
+numpy
+pillow
+python-multipart
+slowapi