up

.dockerignore

@@ -0,0 +1,11 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.env
+*.db
+.git
+.gitignore
+Dockerfile
+.dockerignore
+*.log

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+app/models/MobileNetV3_rust_classifier.keras filter=lfs diff=lfs merge=lfs -text

DockerFile

@@ -0,0 +1,17 @@
+FROM python:3.10
+
+
+WORKDIR /app
+
+COPY . .
+
+
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt  
+
+
+
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] 

app/__init__.py

@@ -0,0 +1,17 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+
+app = FastAPI(title="Corrosion Detection API")
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+from FastAPI.app import routes

app/models/MobileNetV3_rust_classifier.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70b352bc6190b00457c115d366334da322b8657ec51dbb243b41713d4637af45
+size 14252422

app/models/Yolo.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76b28a4116797da8b5c4f724c335f154d28043201398ec6d9e3c60d8cc288978
+size 40835739

app/process.py

@@ -0,0 +1,48 @@
+
+
+from ultralytics import YOLO
+from tensorflow.keras.preprocessing.image import img_to_array
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
+import PIL.Image  as Image
+import io
+import base64
+import os 
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+YOLO_PATH= os.join(BASE_DIR,"models",'Yolo.pt')
+MOBILENET_PATH= os.join(BASE_DIR,"models",'MobileNetV3_rust_classifier.keras')
+
+YOLO_MODEL=YOLO(YOLO_PATH)
+MOBILENET_MODEL=load_model(MOBILENET_PATH)
+
+
+
+def predict(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).resize((224, 224))
+    x = img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+    proba = MOBILENET_MODEL.predict(x)[0]
+    prediction = np.argmax(proba)
+    return prediction,proba[prediction]
+    
+
+def detect(image_bytes):
+    image  = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    result = YOLO_MODEL(image)[0]
+    img_result= Image.fromarray(result.plot()) 
+    buffer=io.BytesIO()
+    img_result.save(buffer,format="PNG")
+    base64_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    uri =f"data:image/png;base64,{base64_str}"
+    return uri
+    
+
+
+
+
+
+

app/routes.py

@@ -0,0 +1,58 @@
+from app import app
+from fastapi.responses import JSONResponse
+from fastapi import UploadFile,File
+from app.process import detect,predict
+
+SUPPORTED_FILES =['png','jpeg','jpg']
+CLASSES = {0:'no rust',1:'rust'}
+
+@app.get("/")
+def index():
+    return JSONResponse({
+        "app":"Rust detection",
+        "version":"1.0.0",
+        "models":[{
+            "Mobilenet":{
+                "size":"Large",
+                "version":3,
+                "accuracy":92.0
+            },
+            "Yolo":{
+                "size":"Medium",
+                "version":9,      
+            },
+            
+        }]
+    })
+
+@app.post("predict",response_class=JSONResponse)
+def upload(image:UploadFile=File(...)):
+    extension = image.filename.split(".")[-1]
+    if extension not in SUPPORTED_FILES:
+        return JSONResponse(content={"error": "Unsupported file type"},status_code=400)
+    
+    image_bytes = image.file.read()
+    try:
+        class_number,probability =predict(image_bytes)
+        uri =None
+        if class_number ==1 :
+            uri =detect(image_bytes)
+        response ={
+            "prediction":CLASSES[class_number],
+            "probability":round(probability,4)*100,
+            "uri":uri
+        }
+        return JSONResponse(content=response,status_code=200)
+    except Exception as exp:
+        print(f"[ERROR] {str(exp)}")
+        return JSONResponse(content={
+            "error":"internal Server error"
+            },status_code=500) 
+        
+    
+
+
+
+
+
+