Update Backend/app.py
Browse files- Backend/app.py +25 -25
Backend/app.py
CHANGED
@@ -1,6 +1,7 @@
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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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@@ -10,23 +11,33 @@ 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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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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#
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model = load_model(model_path)
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# Mount
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app.mount("/static", StaticFiles(directory="Frontend"), name="static")
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#
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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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@@ -37,27 +48,16 @@ emoji_map = {
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6: os.path.join(BASE_DIR, "emojis", "surprised.png")
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}
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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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#
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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 serve_homepage():
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return FileResponse("Frontend/index.html")
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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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@@ -73,7 +73,7 @@ async def process_image(data: ImageData):
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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
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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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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse, FileResponse
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from fastapi.staticfiles import StaticFiles
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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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import os
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import traceback
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from keras.models import load_model
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# Initialize FastAPI app
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app = FastAPI()
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# Mount static files from Frontend
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app.mount("/static", StaticFiles(directory="Frontend"), name="static")
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# Add CORS middleware
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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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# Load model and cascade
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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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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Emoji and emotion maps
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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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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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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6: os.path.join(BASE_DIR, "emojis", "surprised.png")
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}
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# Schema
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class ImageData(BaseModel):
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image: str
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# Serve homepage
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@app.get("/")
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def serve_homepage():
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return FileResponse("Frontend/index.html")
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# Process image
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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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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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