Added home path

app.py

@@ -1,108 +1,111 @@
-from typing import Annotated
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-import uvicorn
-
-from PIL import Image
-
-import torch
-import torch.nn.functional as F
-from torchvision import transforms 
-
-app = FastAPI()
-
-def transform_img(img: Image.Image) -> torch.tensor:
-    # Transformations that will be applied
-    the_transform = transforms.Compose([
-            transforms.Resize((224,224)),
-            transforms.CenterCrop((224,224)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
-    ])
-    return the_transform(img)
-
-# Returns string with class and probability
-def classify_img(img: Image.Image) -> dict:
-    class_names = ['AIR COMPRESSOR',
- 'ALTERNATOR',
- 'BATTERY',
- 'BRAKE CALIPER',
- 'BRAKE PAD',
- 'BRAKE ROTOR',
- 'CAMSHAFT',
- 'CARBERATOR',
- 'CLUTCH PLATE',
- 'COIL SPRING',
- 'CRANKSHAFT',
- 'CYLINDER HEAD',
- 'DISTRIBUTOR',
- 'ENGINE BLOCK',
- 'ENGINE VALVE',
- 'FUEL INJECTOR',
- 'FUSE BOX',
- 'GAS CAP',
- 'HEADLIGHTS',
- 'IDLER ARM',
- 'IGNITION COIL',
- 'INSTRUMENT CLUSTER',
- 'LEAF SPRING',
- 'LOWER CONTROL ARM',
- 'MUFFLER',
- 'OIL FILTER',
- 'OIL PAN',
- 'OIL PRESSURE SENSOR',
- 'OVERFLOW TANK',
- 'OXYGEN SENSOR',
- 'PISTON',
- 'PRESSURE PLATE',
- 'RADIATOR',
- 'RADIATOR FAN',
- 'RADIATOR HOSE',
- 'RADIO',
- 'RIM',
- 'SHIFT KNOB',
- 'SIDE MIRROR',
- 'SPARK PLUG',
- 'SPOILER',
- 'STARTER',
- 'TAILLIGHTS',
- 'THERMOSTAT',
- 'TORQUE CONVERTER',
- 'TRANSMISSION',
- 'VACUUM BRAKE BOOSTER',
- 'VALVE LIFTER',
- 'WATER PUMP',
- 'WINDOW REGULATOR']
-    model = torch.jit.load("car_part_traced_classifier_resnet50.ptl")
-    # Applying transformation to the image
-    model_img = transform_img(img)
-    model_img = model_img.view(1,3,224,224)
-    
-    # Running image through the model
-    model.eval()
-    with torch.no_grad():
-        result = model(model_img)
-    
-    # Converting values to softmax values 
-    result = F.softmax(result,dim=1)
-    # Grabbing top 3 indices and probabilities for each index
-    top3_prob, top3_catid = torch.topk(result,3)
-    
-    # Dictionary I will display
-    model_output = {}
-    for i in range(top3_prob.size(1)):
-        car_part_name = class_names[top3_catid[0][i].item()]
-        probability = round(top3_prob[0][i].item() * 100, 2)
-        
-        model_output[f"top{i+1}"] = {"name": car_part_name, "probability": probability}
-    
-    return model_output
-
-@app.post("/upload", response_model=dict)
-async def upload(file: UploadFile = File(...)) -> JSONResponse:
-
-    pil_img = Image.open(file.file)
-    result = classify_img(pil_img)
-    
-    return JSONResponse(content=result, status_code=201)
-
+from typing import Annotated
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+import uvicorn
+
+from PIL import Image
+
+import torch
+import torch.nn.functional as F
+from torchvision import transforms 
+
+app = FastAPI()
+
+def transform_img(img: Image.Image) -> torch.tensor:
+    # Transformations that will be applied
+    the_transform = transforms.Compose([
+            transforms.Resize((224,224)),
+            transforms.CenterCrop((224,224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
+    ])
+    return the_transform(img)
+
+# Returns string with class and probability
+def classify_img(img: Image.Image) -> dict:
+    class_names = ['AIR COMPRESSOR',
+ 'ALTERNATOR',
+ 'BATTERY',
+ 'BRAKE CALIPER',
+ 'BRAKE PAD',
+ 'BRAKE ROTOR',
+ 'CAMSHAFT',
+ 'CARBERATOR',
+ 'CLUTCH PLATE',
+ 'COIL SPRING',
+ 'CRANKSHAFT',
+ 'CYLINDER HEAD',
+ 'DISTRIBUTOR',
+ 'ENGINE BLOCK',
+ 'ENGINE VALVE',
+ 'FUEL INJECTOR',
+ 'FUSE BOX',
+ 'GAS CAP',
+ 'HEADLIGHTS',
+ 'IDLER ARM',
+ 'IGNITION COIL',
+ 'INSTRUMENT CLUSTER',
+ 'LEAF SPRING',
+ 'LOWER CONTROL ARM',
+ 'MUFFLER',
+ 'OIL FILTER',
+ 'OIL PAN',
+ 'OIL PRESSURE SENSOR',
+ 'OVERFLOW TANK',
+ 'OXYGEN SENSOR',
+ 'PISTON',
+ 'PRESSURE PLATE',
+ 'RADIATOR',
+ 'RADIATOR FAN',
+ 'RADIATOR HOSE',
+ 'RADIO',
+ 'RIM',
+ 'SHIFT KNOB',
+ 'SIDE MIRROR',
+ 'SPARK PLUG',
+ 'SPOILER',
+ 'STARTER',
+ 'TAILLIGHTS',
+ 'THERMOSTAT',
+ 'TORQUE CONVERTER',
+ 'TRANSMISSION',
+ 'VACUUM BRAKE BOOSTER',
+ 'VALVE LIFTER',
+ 'WATER PUMP',
+ 'WINDOW REGULATOR']
+    model = torch.jit.load("car_part_traced_classifier_resnet50.ptl")
+    # Applying transformation to the image
+    model_img = transform_img(img)
+    model_img = model_img.view(1,3,224,224)
+    
+    # Running image through the model
+    model.eval()
+    with torch.no_grad():
+        result = model(model_img)
+    
+    # Converting values to softmax values 
+    result = F.softmax(result,dim=1)
+    # Grabbing top 3 indices and probabilities for each index
+    top3_prob, top3_catid = torch.topk(result,3)
+    
+    # Dictionary I will display
+    model_output = {}
+    for i in range(top3_prob.size(1)):
+        car_part_name = class_names[top3_catid[0][i].item()]
+        probability = round(top3_prob[0][i].item() * 100, 2)
+        
+        model_output[f"top{i+1}"] = {"name": car_part_name, "probability": probability}
+    
+    return model_output
+
+@app.post("/upload", response_model=dict)
+async def upload(file: UploadFile = File(...)) -> JSONResponse:
+
+    pil_img = Image.open(file.file)
+    result = classify_img(pil_img)
+    
+    return JSONResponse(content=result, status_code=201)
+
+@app.get("/")
+def api_home()
+    return JSONResponse({'detail': 'Welcome to FastAPI!'}, status_code=200)