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from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
import numpy as np
import joblib
import cv2
from PIL import Image
import mediapipe as mp
import os
import io
import math
app = FastAPI()
# ---------------- LOAD MODEL ----------------
MODEL_DIR = "model2"
model = joblib.load(os.path.join(MODEL_DIR, "emotion_model.joblib"))
label_encoder = joblib.load(os.path.join(MODEL_DIR, "label_encoder.joblib"))
face_mesh = mp.solutions.face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=1,
min_detection_confidence=0.5
)
# ---------------- FEATURE ORDER (FULL LIST) ----------------
FEATURE_ORDER = [
"mouth_width", "mouth_height",
"left_eye_width", "left_eye_height",
"right_eye_width", "right_eye_height",
"left_eyebrow_height", "right_eyebrow_height",
"lip_top_height", "lip_bottom_height",
"nose_length", "face_width"
]
# ---------------- FEATURE EXTRACTION FUNCTIONS ----------------
def euclidean(p1, p2):
return math.sqrt(
(p1[0] - p2[0]) ** 2 +
(p1[1] - p2[1]) ** 2 +
(p1[2] - p2[2]) ** 2
)
def compute_basic_features(landmarks, w, h):
"""Compute all required geometric features."""
def lx(i): return landmarks[i][0] * w
def ly(i): return landmarks[i][1] * h
def lz(i): return landmarks[i][2]
def point(i): return (lx(i), ly(i), lz(i))
features = {}
# Mouth metrics
features["mouth_width"] = euclidean(point(61), point(291))
features["mouth_height"] = euclidean(point(0), point(17))
# Left eye metrics
features["left_eye_width"] = euclidean(point(33), point(133))
features["left_eye_height"] = euclidean(point(159), point(145))
# Right eye metrics
features["right_eye_width"] = euclidean(point(362), point(263))
features["right_eye_height"] = euclidean(point(386), point(374))
# Eyebrows
features["left_eyebrow_height"] = euclidean(point(70), point(105))
features["right_eyebrow_height"] = euclidean(point(300), point(334))
# Lip heights
features["lip_top_height"] = euclidean(point(13), point(14))
features["lip_bottom_height"] = euclidean(point(14), point(18))
# Nose
features["nose_length"] = euclidean(point(1), point(197))
# Face width
features["face_width"] = euclidean(point(127), point(356))
return features
def extract_features(image):
img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = face_mesh.process(img_rgb)
if not results.multi_face_landmarks:
return None, ["No face detected"]
landmarks = np.array(
[(lm.x, lm.y, lm.z) for lm in results.multi_face_landmarks[0].landmark]
)
h, w, _ = image.shape
features = compute_basic_features(landmarks, w, h)
# reorder features
ordered = [features.get(f, 0) for f in FEATURE_ORDER]
return np.array(ordered).reshape(1, -1), None
def predict_emotion(image):
X, error = extract_features(image)
if error:
return {"error": error}
pred = model.predict(X)[0]
prob = model.predict_proba(X).max()
emotion = label_encoder.inverse_transform([pred])[0]
return {
"emotion": emotion,
"confidence": float(prob)
}
# ---------------- API ENDPOINT ----------------
@app.post("/predict")
async def predict(image: UploadFile = File(...)):
try:
img_bytes = await image.read()
img = Image.open(io.BytesIO(img_bytes))
img = np.array(img)
# standardize image format
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif img.shape[2] == 4:
img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
else:
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
result = predict_emotion(img)
return JSONResponse(result)
except Exception as e:
return JSONResponse({"error": str(e)}, status_code=500)
@app.get("/")
def root():
return {"status": "API running", "endpoint": "/predict"}
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