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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.get("/") |
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def read_root(): |
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return {"message": "FaceFeel backend is running"} |
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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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