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