major fixes for image

.gitignore

@@ -1 +1,2 @@
-/.venv/*
+/.venv/*
+/flagged/*

app.py

@@ -40,9 +40,9 @@ def main_pipeline(image):
 
     #image = im_b64.file.read()
 
-    image = Image.open(io.BytesIO(image))
+    #image = Image.open(image)
 
-    image = image.convert("RGB")
+    #image = image.convert("RGB")
 
     image = np.asarray(image)
 
@@ -62,7 +62,8 @@ def main_pipeline(image):
     #cv2.imshow('image', np.asarray(image))
     #cv2.waitKey()
 
-    image = tf.image.resize(image, [224,224]) 
+    image = image.resize((224,224))
+    #image = tf.image.resize(image, [224,224]) 
     image = tf.keras.preprocessing.image.img_to_array(image)
     image = image / 255.0      
     image = tf.expand_dims(image, axis=0)
@@ -93,7 +94,7 @@ def main_pipeline(image):
     return predictions_json
 
 # initializing the input component
-image = gr.inputs.Image(shape = (299, 299, 3))
+image = gr.inputs.Image(shape = (224, 224))
 # initializing the output component
 labels = gr.outputs.Label()
 

server.py

@@ -1,154 +0,0 @@
-import json
-import os
-from fastapi import FastAPI, Request
-from matplotlib import pyplot as plt
-from PIL import Image
-from fastapi.middleware.cors import CORSMiddleware
-
-import tensorflow as tf
-import numpy as np
-import yaml
-import io
-import cv2
-
-
-# read config file
-def read_config():
-    config = {}
-    print(os.path.curdir)
-    with open(os.path.join('api','models_config.yaml'), 'r') as cf:
-        config = yaml.safe_load(cf)
-
-    for var in config:
-        config[var] = config[var].replace(';', os.sep)
-
-    return config
-
-
-# create app and load model
-config = read_config()
-service = FastAPI()
-
-# read path to test images
-test_img = [os.path.join(config['TEST_IMG_PATH'], 'test1.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test2.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test3.jpg'),
-os.path.join(config['TEST_IMG_PATH'], 'test4.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test5.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test6.jpg'),
-os.path.join(config['TEST_IMG_PATH'], 'test7.jpg')]
-
-# load pretrained models
-age_model = tf.keras.models.load_model(config['A_M_PATH'])
-gender_model = tf.keras.models.load_model(config['G_M_PATH'])
-face_cascade = cv2.CascadeClassifier(config['FD_M_PATH'])
-
-
-# add CORS middleware
-service.add_middleware(
-    CORSMiddleware,
-    allow_origins=['*']
-)
-
-
-# status route
-@service.get("/checkstatus")
-async def read_root():
-    """
-        Check status of routes
-    """
-
-    url_list = [{"path": route.path, "name": route.name} for route in service.routes]
-
-    if len(url_list) == 7:
-        return "Healthy"
-    else:
-        return "Unhealthy"
-
-
-
-async def make_predictions_pipeline(request, from_slider= False):
-    
-    if from_slider:
-        file = await request.form()
-        img_ind = file['img']
-        image = Image.open(test_img[int(img_ind)])
-    else:
-        file = await request.form()
-        im_b64 = file['img']
-
-        image = im_b64.file.read()
-        image = Image.open(io.BytesIO(image))
-        image = image.convert("RGB")
-
-    image = np.asarray(image)
-
-    gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    faces = face_cascade.detectMultiScale(gray_img, 1.1, 4)
-    
-    if len(faces) == 0:
-        return "NO FACE DETECTED"
-
-    x, y, w, h =  faces[0]
-    #cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
-
-
-    image = Image.fromarray(image)
-    image = image.crop((x, y, x + w, y + h))
-
-    #cv2.imshow('image', np.asarray(image))
-    #cv2.waitKey()
-
-    image = tf.image.resize(image, [224,224]) 
-    image = tf.keras.preprocessing.image.img_to_array(image)
-    image = image / 255.0      
-    image = tf.expand_dims(image, axis=0)
-
-    age_prds = age_model.predict(image)
-    gender_prds = gender_model.predict(image)
-
-    age_prds = np.around(age_prds)
-    gender_prds = np.around(gender_prds)
-    gender = ""
-
-    if gender_prds[0][0] == 0:
-        gender = 'male'
-    else:
-        gender = 'female'
-
-    data = {}
-    data['age'] = str(age_prds[0][0])
-    data['gender'] = gender
-    b_box_arr = [(x, y, w, h) for (x, y, w, h) in faces]
-
-    data['left'] = str(b_box_arr[0][0])
-    data['top'] =  str(b_box_arr[0][1])
-    data['width'] = str(b_box_arr[0][2])
-    data['height'] = str(b_box_arr[0][3])
-    predictions_json = json.dumps(data)
-
-    return predictions_json
-
-
-# slider predictions route
-@service.post("/api/predictions_slider")
-async def receive_image(request: Request):
-    """
-        Function for predicts from slider
-    """ 
-    results_json = await make_predictions_pipeline(request, from_slider= True)
-    return results_json
-
-
-
-# main predictions route
-@service.post("/api/predictions")
-async def receive_image(request: Request):
-    """
-        Function for predicts
-        
-        Example request:
-
-        img = open( FILEPATH , 'rb')
-        files = {'img': img}
-        resp = requests.post("http://{host:port}/api/predictions", files= files)
-    """ 
-
-    results_json = await make_predictions_pipeline(request, from_slider= False)
-    return results_json