Update main.py

main.py

@@ -1,67 +1,71 @@
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.middleware.cors import CORSMiddleware
-import json
-import tempfile
-import shutil
-import os
-from single_person_processor import SinglePersonPredictor
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["http://localhost:3000"],  # Change this to match your frontend URL
-    allow_credentials=True,
-    allow_methods=["*"],  # Allow all methods (POST, GET, etc.)
-    allow_headers=["*"],  # Allow all headers
-)
-
-
-# Initialize the predictor
-predictor = SinglePersonPredictor(model_path="best_model.keras")
-
-@app.post("/predict/")
-async def predict(
-    front_image: UploadFile = File(...),  
-    side_image: UploadFile = File(...),  
-    input_data: str = Form(...)  
-):
-    try:
-        try:
-            input_dict = json.loads(input_data)
-        except json.JSONDecodeError:
-            return {"error": "Invalid JSON format in input_data"}
-
-        # Validate file types
-        if front_image.content_type not in ["image/jpeg", "image/png"]:
-            return {"error": "Front image must be a JPEG or PNG file"}
-        if side_image.content_type not in ["image/jpeg", "image/png"]:
-            return {"error": "Side image must be a JPEG or PNG file"}
-
-        # Create temporary files for images
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as front_temp:
-            shutil.copyfileobj(front_image.file, front_temp)
-            front_temp_path = front_temp.name
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as side_temp:
-            shutil.copyfileobj(side_image.file, side_temp)
-            side_temp_path = side_temp.name
-
-        # Perform predictions
-        results = predictor.predict_measurements(
-            front_img_path=front_temp_path,
-            side_img_path=side_temp_path,
-            gender=input_dict.get("gender"),
-            height_cm=input_dict.get("height_cm"),
-            weight_kg=input_dict.get("weight_kg"),
-            apparel_type=input_dict.get("apparel_type")
-        )
-
-        # Clean up temporary files
-        os.remove(front_temp_path)
-        os.remove(side_temp_path)
-        
-        return {"results": results}
-
-    except Exception as e:
-        return {"error": str(e)}
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.middleware.cors import CORSMiddleware
+import json
+import tempfile
+import shutil
+import os
+from single_person_processor import SinglePersonPredictor
+
+@app.get("/")
+async def root():
+    return {"message": "FastAPI is running!"}
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000"],  # Change this to match your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all methods (POST, GET, etc.)
+    allow_headers=["*"],  # Allow all headers
+)
+
+
+# Initialize the predictor
+predictor = SinglePersonPredictor(model_path="best_model.keras")
+
+@app.post("/predict/")
+async def predict(
+    front_image: UploadFile = File(...),  
+    side_image: UploadFile = File(...),  
+    input_data: str = Form(...)  
+):
+    try:
+        try:
+            input_dict = json.loads(input_data)
+        except json.JSONDecodeError:
+            return {"error": "Invalid JSON format in input_data"}
+
+        # Validate file types
+        if front_image.content_type not in ["image/jpeg", "image/png"]:
+            return {"error": "Front image must be a JPEG or PNG file"}
+        if side_image.content_type not in ["image/jpeg", "image/png"]:
+            return {"error": "Side image must be a JPEG or PNG file"}
+
+        # Create temporary files for images
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as front_temp:
+            shutil.copyfileobj(front_image.file, front_temp)
+            front_temp_path = front_temp.name
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as side_temp:
+            shutil.copyfileobj(side_image.file, side_temp)
+            side_temp_path = side_temp.name
+
+        # Perform predictions
+        results = predictor.predict_measurements(
+            front_img_path=front_temp_path,
+            side_img_path=side_temp_path,
+            gender=input_dict.get("gender"),
+            height_cm=input_dict.get("height_cm"),
+            weight_kg=input_dict.get("weight_kg"),
+            apparel_type=input_dict.get("apparel_type")
+        )
+
+        # Clean up temporary files
+        os.remove(front_temp_path)
+        os.remove(side_temp_path)
+        
+        return {"results": results}
+
+    except Exception as e:
+        return {"error": str(e)}