Update Backend/app.py

Backend/app.py

@@ -1,92 +1,96 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-import base64
-from io import BytesIO
-from PIL import Image
-import numpy as np
-import cv2
-import os
-import traceback
-from keras.models import load_model
-
-# Load the trained model
-model_path = os.path.join(os.path.dirname(__file__), 'emotion_model.keras')
-model = load_model(model_path)
-
-# Emotion and emoji maps
-emotion_dict = {
-    0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy",
-    4: "Neutral", 5: "Sad", 6: "Surprised"
-}
-
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-emoji_map = {
-    0: os.path.join(BASE_DIR, "emojis", "angry.png"),
-    1: os.path.join(BASE_DIR, "emojis", "disgusted.png"),
-    2: os.path.join(BASE_DIR, "emojis", "fearful.png"),
-    3: os.path.join(BASE_DIR, "emojis", "happy.png"),
-    4: os.path.join(BASE_DIR, "emojis", "neutral.png"),
-    5: os.path.join(BASE_DIR, "emojis", "sad.png"),
-    6: os.path.join(BASE_DIR, "emojis", "surprised.png")
-}
-
-# Initialize FastAPI app
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-class ImageData(BaseModel):
-    image: str
-
-# Load Haarcascade once
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-
-@app.post("/process-image")
-async def process_image(data: ImageData):
-    try:
-        header, encoded = data.image.split(",")
-        img_bytes = base64.b64decode(encoded)
-        img = Image.open(BytesIO(img_bytes)).convert('RGB')
-        img_np = np.array(img)
-
-        gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
-        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
-
-        if len(faces) == 0:
-            raise HTTPException(status_code=400, detail="No face detected")
-
-        for (x, y, w, h) in faces:
-            roi_gray = gray[y:y + h, x:x + w]
-            roi = cv2.resize(roi_gray, (48, 48))
-            roi = roi.astype("float") / 255.0
-            roi = np.expand_dims(roi, axis=-1)
-            roi = np.expand_dims(roi, axis=0)
-
-            preds = model.predict(roi, verbose=0)
-            emotion_index = int(np.argmax(preds))
-            emotion_label = emotion_dict[emotion_index]
-
-            emoji_path = emoji_map[emotion_index]
-            emoji_img = Image.open(emoji_path).convert("RGBA")
-            buffer = BytesIO()
-            emoji_img.save(buffer, format="PNG")
-            encoded_emoji = base64.b64encode(buffer.getvalue()).decode("utf-8")
-
-            return JSONResponse({
-                "emotion": emotion_label,
-                "emoji": f"data:image/png;base64,{encoded_emoji}"
-            })
-
-        raise HTTPException(status_code=400, detail="Face not processed")
-
-    except Exception as e:
-        print("Error:", str(e))
-        traceback.print_exc()
-        raise HTTPException(status_code=500, detail=str(e))
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+import base64
+from io import BytesIO
+from PIL import Image
+import numpy as np
+import cv2
+import os
+import traceback
+from keras.models import load_model
+
+# Load the trained model
+model_path = os.path.join(os.path.dirname(__file__), 'emotion_model.keras')
+model = load_model(model_path)
+
+# Emotion and emoji maps
+emotion_dict = {
+    0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy",
+    4: "Neutral", 5: "Sad", 6: "Surprised"
+}
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+emoji_map = {
+    0: os.path.join(BASE_DIR, "emojis", "angry.png"),
+    1: os.path.join(BASE_DIR, "emojis", "disgusted.png"),
+    2: os.path.join(BASE_DIR, "emojis", "fearful.png"),
+    3: os.path.join(BASE_DIR, "emojis", "happy.png"),
+    4: os.path.join(BASE_DIR, "emojis", "neutral.png"),
+    5: os.path.join(BASE_DIR, "emojis", "sad.png"),
+    6: os.path.join(BASE_DIR, "emojis", "surprised.png")
+}
+
+# Initialize FastAPI app
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class ImageData(BaseModel):
+    image: str
+
+# Load Haarcascade once
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+@app.get("/")
+def read_root():
+    return {"message": "FaceFeel backend is running"}
+
+@app.post("/process-image")
+async def process_image(data: ImageData):
+    try:
+        header, encoded = data.image.split(",")
+        img_bytes = base64.b64decode(encoded)
+        img = Image.open(BytesIO(img_bytes)).convert('RGB')
+        img_np = np.array(img)
+
+        gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+
+        if len(faces) == 0:
+            raise HTTPException(status_code=400, detail="No face detected")
+
+        for (x, y, w, h) in faces:
+            roi_gray = gray[y:y + h, x:x + w]
+            roi = cv2.resize(roi_gray, (48, 48))
+            roi = roi.astype("float") / 255.0
+            roi = np.expand_dims(roi, axis=-1)
+            roi = np.expand_dims(roi, axis=0)
+
+            preds = model.predict(roi, verbose=0)
+            emotion_index = int(np.argmax(preds))
+            emotion_label = emotion_dict[emotion_index]
+
+            emoji_path = emoji_map[emotion_index]
+            emoji_img = Image.open(emoji_path).convert("RGBA")
+            buffer = BytesIO()
+            emoji_img.save(buffer, format="PNG")
+            encoded_emoji = base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+            return JSONResponse({
+                "emotion": emotion_label,
+                "emoji": f"data:image/png;base64,{encoded_emoji}"
+            })
+
+        raise HTTPException(status_code=400, detail="Face not processed")
+
+    except Exception as e:
+        print("Error:", str(e))
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=str(e))