w

Dockerfile

@@ -0,0 +1,12 @@
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn","-b","0.0.0.0:7860" "main:app"]

emotion_recognition/ai_model_photo.py

@@ -0,0 +1,101 @@
+# Detecte Emotion By photo
+import cv2
+import os
+from keras.models import model_from_json
+import numpy as np
+import matplotlib.pyplot as plt
+import threading
+
+
+def display_image(image_array):
+    cv2.imshow('My Image', image_array)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# os.chdir('models')
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful",
+                3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# load json and create model
+# put your model path there
+json_file = open(
+    "emotion_recognition/models/emotion_model(MyNet0.82).json", 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+emotion_model = model_from_json(loaded_model_json)
+
+# load weights into new model
+# put your weight path there
+emotion_model.load_weights(
+    "emotion_recognition/models/emotion_model(MyNet0.82).h5")
+print("Loaded model from disk")
+
+
+def detectFace(net, frame, confidence_threshold=0.7):
+    frameOpencvDNN = frame.copy()
+    print(frameOpencvDNN.shape)
+    frameHeight = frameOpencvDNN.shape[0]
+    frameWidth = frameOpencvDNN.shape[1]
+    blob = cv2.dnn.blobFromImage(frameOpencvDNN, 1.0, (227, 227), [
+                                 124.96, 115.97, 106.13], swapRB=True, crop=False)
+    net.setInput(blob)
+    detections = net.forward()
+    faceBoxes = []
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+        if confidence > confidence_threshold:
+            x1 = int(detections[0, 0, i, 3]*frameWidth)
+            y1 = int(detections[0, 0, i, 4]*frameHeight)
+            x2 = int(detections[0, 0, i, 5]*frameWidth)
+            y2 = int(detections[0, 0, i, 6]*frameHeight)
+            print("x1=", x1, " x2=", x2, " y1=", y1, " y2=", y2)
+            faceBoxes.append([x1, y1, x2, y2])
+            cv2.rectangle(frameOpencvDNN, (x1, y1), (x2, y2),
+                          (0, 255, 0), int(round(frameHeight/150)), 8)
+    return frameOpencvDNN, faceBoxes
+
+
+faceProto = 'emotion_recognition/models/opencv_face_detector.pbtxt'
+faceModel = 'emotion_recognition/models/opencv_face_detector_uint8.pb'
+# Loding detecting face model
+faceNet = cv2.dnn.readNet(faceModel, faceProto)
+
+# Get a Test image and process it to send it to model
+
+
+def ai(path):
+    f = cv2.imread(path, cv2.IMREAD_COLOR)
+    # cv2.imshow("fla",f)
+    gray_frame = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
+    resultImg, faceBoxes = detectFace(faceNet, f)
+    print('faceBoxes', faceBoxes)
+
+    # Get the cordnate of face
+    x1, y1, x2, y2 = faceBoxes[0][0], faceBoxes[0][1], faceBoxes[0][2], faceBoxes[0][3]
+    print("x , y , w , h", x1, y1, x2, y2)
+
+    roi_gray_frame = gray_frame[y1-20:y2+10, x1-20:x2+10]
+    cropped_img = np.expand_dims(np.expand_dims(
+        cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+    img_resized = cv2.resize(resultImg, (0, 0), fx=0.5,
+                             fy=0.5, interpolation=cv2.INTER_AREA)
+    # send photo to model
+    emotion_prediction = emotion_model.predict(cropped_img)
+
+    # Get the result
+    maxindex = int(np.argmax(emotion_prediction))
+    cv2.putText(img_resized, emotion_dict[maxindex], (x1+5, y1-20),
+                cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+    # cv2.imshow("crop",resultImg)
+    # cv2.resizeWindow("crop",720,460)
+    # cv2.imshow("crop2",roi_gray_frame)
+    # cv2.resizeWindow("crop2",720,460)
+    print("emotion_prediction=", emotion_dict[maxindex])
+
+    display_thread = threading.Thread(
+        target=display_image, args=(img_resized,))
+    display_thread.start()
+
+    return emotion_dict[maxindex]

emotion_recognition/ai_model_real_time.py

@@ -0,0 +1,94 @@
+# Detecte Emotion in Real Time
+import cv2
+import os
+from keras.models import model_from_json
+import numpy as np
+os.chdir('D:/Desktop/CAE/fifth/graduation_project/python/emotion_recognition/models')
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful",
+                3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# load json and create model
+# put your model path there
+json_file = open(
+    'D:/Desktop/CAE/fifth/graduation_project/python/emotion_recognition/models/emotion_model(MyNet0.82).json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+emotion_model = model_from_json(loaded_model_json)
+
+# load weights into new model
+# put your weight path there
+emotion_model.load_weights(
+    "D:/Desktop/CAE/fifth/graduation_project/python/emotion_recognition/models/emotion_model(MyNet0.82).h5")
+print("Loaded model from disk")
+
+
+def detectFace(net, frame, confidence_threshold=0.7):
+    frameOpencvDNN = frame.copy()
+    print(frameOpencvDNN.shape)
+    frameHeight = frameOpencvDNN.shape[0]
+    frameWidth = frameOpencvDNN.shape[1]
+    blob = cv2.dnn.blobFromImage(frameOpencvDNN, 1.0, (227, 227), [
+                                 124.96, 115.97, 106.13], swapRB=True, crop=False)
+    net.setInput(blob)
+    detections = net.forward()
+    faceBoxes = []
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+        if confidence > confidence_threshold:
+            x1 = int(detections[0, 0, i, 3]*frameWidth)
+            y1 = int(detections[0, 0, i, 4]*frameHeight)
+            x2 = int(detections[0, 0, i, 5]*frameWidth)
+            y2 = int(detections[0, 0, i, 6]*frameHeight)
+            if ((x1 > 60) and (x2 > 50) and (y1 > 40) and (y2 > 40)):
+                print("x1=", x1, " x2=", x2, " y1=", y1, " y2=", y2)
+                faceBoxes.append([x1, y1, x2, y2])
+            else:
+                continue
+            cv2.rectangle(frameOpencvDNN, (x1, y1), (x2, y2),
+                          (0, 255, 0), int(round(frameHeight/150)), 8)
+    return frameOpencvDNN, faceBoxes
+
+
+faceProto = './opencv_face_detector.pbtxt'
+faceModel = './opencv_face_detector_uint8.pb'
+# Loding detecting face model
+faceNet = cv2.dnn.readNet(faceModel, faceProto)
+
+# Get a Test image and process it to send it to model
+
+
+def ai():
+    cap = cv2.VideoCapture(0)
+
+    while cv2.waitKey(1) < 0:
+        hasFrame, frame = cap.read()
+        if not hasFrame:
+            cv2.waitKey()
+            break
+
+        resultImg, faceBoxes = detectFace(faceNet, frame)
+
+        if not faceBoxes:
+            print("No face detected")
+
+        for (x1, y1, x2, y2) in faceBoxes:
+            gray_frame = cv2.cvtColor(resultImg, cv2.COLOR_BGR2GRAY)
+            print("x , y , w , h", x1, y1, x2, y2)
+            roi_gray_frame = gray_frame[y1-20:y2+10, x1-20:x2+10]
+            cropped_img = np.expand_dims(np.expand_dims(
+                cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+            emotion_prediction = emotion_model.predict(cropped_img)
+            maxindex = int(np.argmax(emotion_prediction))
+            cv2.putText(resultImg, emotion_dict[maxindex], (
+                x1+5, y1-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+        cv2.imshow("Detecting age and Gender", resultImg)
+
+        if cv2.waitKey(33) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+ai()

emotion_recognition/info.txt

@@ -0,0 +1,4 @@
+this is a trial file to add some lines
+the main purpose of this file is testing our own chatbot
+this bot isn't for answering general questions
+here in computer and automation engineering we are learning about large language model 

emotion_recognition/models/emotion_model(MyNet0.82).h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82efa51bfa39bf9540312a61e7220664b87ee7f4687d78d6b59376f7153458f8
+size 9423448

emotion_recognition/models/emotion_model(MyNet0.82).json

@@ -0,0 +1 @@
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emotion_recognition/models/opencv_face_detector.pbtxt

@@ -0,0 +1,2362 @@
+node {
+  name: "data"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "data_bn/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "data:0"
+  input: "data_bn/gamma"
+  input: "data_bn/beta"
+  input: "data_bn/mean"
+  input: "data_bn/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "data_scale/Mul"
+  op: "Mul"
+  input: "data_bn/FusedBatchNorm"
+  input: "data_scale/mul"
+}
+node {
+  name: "data_scale/BiasAdd"
+  op: "BiasAdd"
+  input: "data_scale/Mul"
+  input: "data_scale/add"
+}
+node {
+  name: "SpaceToBatchND/block_shape"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 2
+          }
+        }
+        int_val: 1
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "SpaceToBatchND/paddings"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 2
+          }
+          dim {
+            size: 2
+          }
+        }
+        int_val: 3
+        int_val: 3
+        int_val: 3
+        int_val: 3
+      }
+    }
+  }
+}
+node {
+  name: "Pad"
+  op: "SpaceToBatchND"
+  input: "data_scale/BiasAdd"
+  input: "SpaceToBatchND/block_shape"
+  input: "SpaceToBatchND/paddings"
+}
+node {
+  name: "conv1_h/Conv2D"
+  op: "Conv2D"
+  input: "Pad"
+  input: "conv1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "VALID"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv1_h/Conv2D"
+  input: "conv1_h/bias"
+}
+node {
+  name: "BatchToSpaceND"
+  op: "BatchToSpaceND"
+  input: "conv1_h/BiasAdd"
+}
+node {
+  name: "conv1_bn_h/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "BatchToSpaceND"
+  input: "conv1_bn_h/gamma"
+  input: "conv1_bn_h/beta"
+  input: "conv1_bn_h/mean"
+  input: "conv1_bn_h/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "conv1_scale_h/Mul"
+  op: "Mul"
+  input: "conv1_bn_h/FusedBatchNorm"
+  input: "conv1_scale_h/mul"
+}
+node {
+  name: "conv1_scale_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv1_scale_h/Mul"
+  input: "conv1_scale_h/add"
+}
+node {
+  name: "Relu"
+  op: "Relu"
+  input: "conv1_scale_h/BiasAdd"
+}
+node {
+  name: "conv1_pool/MaxPool"
+  op: "MaxPool"
+  input: "Relu"
+  attr {
+    key: "ksize"
+    value {
+      list {
+        i: 1
+        i: 3
+        i: 3
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_64_1_conv1_h/Conv2D"
+  op: "Conv2D"
+  input: "conv1_pool/MaxPool"
+  input: "layer_64_1_conv1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_64_1_bn2_h/FusedBatchNorm"
+  op: "BiasAdd"
+  input: "layer_64_1_conv1_h/Conv2D"
+  input: "layer_64_1_conv1_h/Conv2D_bn_offset"
+}
+node {
+  name: "layer_64_1_scale2_h/Mul"
+  op: "Mul"
+  input: "layer_64_1_bn2_h/FusedBatchNorm"
+  input: "layer_64_1_scale2_h/mul"
+}
+node {
+  name: "layer_64_1_scale2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_64_1_scale2_h/Mul"
+  input: "layer_64_1_scale2_h/add"
+}
+node {
+  name: "Relu_1"
+  op: "Relu"
+  input: "layer_64_1_scale2_h/BiasAdd"
+}
+node {
+  name: "layer_64_1_conv2_h/Conv2D"
+  op: "Conv2D"
+  input: "Relu_1"
+  input: "layer_64_1_conv2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "add"
+  op: "Add"
+  input: "layer_64_1_conv2_h/Conv2D"
+  input: "conv1_pool/MaxPool"
+}
+node {
+  name: "layer_128_1_bn1_h/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "add"
+  input: "layer_128_1_bn1_h/gamma"
+  input: "layer_128_1_bn1_h/beta"
+  input: "layer_128_1_bn1_h/mean"
+  input: "layer_128_1_bn1_h/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "layer_128_1_scale1_h/Mul"
+  op: "Mul"
+  input: "layer_128_1_bn1_h/FusedBatchNorm"
+  input: "layer_128_1_scale1_h/mul"
+}
+node {
+  name: "layer_128_1_scale1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_128_1_scale1_h/Mul"
+  input: "layer_128_1_scale1_h/add"
+}
+node {
+  name: "Relu_2"
+  op: "Relu"
+  input: "layer_128_1_scale1_h/BiasAdd"
+}
+node {
+  name: "layer_128_1_conv_expand_h/Conv2D"
+  op: "Conv2D"
+  input: "Relu_2"
+  input: "layer_128_1_conv_expand_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_128_1_conv1_h/Conv2D"
+  op: "Conv2D"
+  input: "Relu_2"
+  input: "layer_128_1_conv1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_128_1_bn2/FusedBatchNorm"
+  op: "BiasAdd"
+  input: "layer_128_1_conv1_h/Conv2D"
+  input: "layer_128_1_conv1_h/Conv2D_bn_offset"
+}
+node {
+  name: "layer_128_1_scale2/Mul"
+  op: "Mul"
+  input: "layer_128_1_bn2/FusedBatchNorm"
+  input: "layer_128_1_scale2/mul"
+}
+node {
+  name: "layer_128_1_scale2/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_128_1_scale2/Mul"
+  input: "layer_128_1_scale2/add"
+}
+node {
+  name: "Relu_3"
+  op: "Relu"
+  input: "layer_128_1_scale2/BiasAdd"
+}
+node {
+  name: "layer_128_1_conv2/Conv2D"
+  op: "Conv2D"
+  input: "Relu_3"
+  input: "layer_128_1_conv2/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "add_1"
+  op: "Add"
+  input: "layer_128_1_conv2/Conv2D"
+  input: "layer_128_1_conv_expand_h/Conv2D"
+}
+node {
+  name: "layer_256_1_bn1/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "add_1"
+  input: "layer_256_1_bn1/gamma"
+  input: "layer_256_1_bn1/beta"
+  input: "layer_256_1_bn1/mean"
+  input: "layer_256_1_bn1/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "layer_256_1_scale1/Mul"
+  op: "Mul"
+  input: "layer_256_1_bn1/FusedBatchNorm"
+  input: "layer_256_1_scale1/mul"
+}
+node {
+  name: "layer_256_1_scale1/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_256_1_scale1/Mul"
+  input: "layer_256_1_scale1/add"
+}
+node {
+  name: "Relu_4"
+  op: "Relu"
+  input: "layer_256_1_scale1/BiasAdd"
+}
+node {
+  name: "SpaceToBatchND_1/paddings"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 2
+          }
+          dim {
+            size: 2
+          }
+        }
+        int_val: 1
+        int_val: 1
+        int_val: 1
+        int_val: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_256_1_conv_expand/Conv2D"
+  op: "Conv2D"
+  input: "Relu_4"
+  input: "layer_256_1_conv_expand/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv4_3_norm/l2_normalize"
+  op: "L2Normalize"
+  input: "Relu_4:0"
+  input: "conv4_3_norm/l2_normalize/Sum/reduction_indices"
+}
+node {
+  name: "conv4_3_norm/mul_1"
+  op: "Mul"
+  input: "conv4_3_norm/l2_normalize"
+  input: "conv4_3_norm/mul"
+}
+node {
+  name: "conv4_3_norm_mbox_loc/Conv2D"
+  op: "Conv2D"
+  input: "conv4_3_norm/mul_1"
+  input: "conv4_3_norm_mbox_loc/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv4_3_norm_mbox_loc/BiasAdd"
+  op: "BiasAdd"
+  input: "conv4_3_norm_mbox_loc/Conv2D"
+  input: "conv4_3_norm_mbox_loc/bias"
+}
+node {
+  name: "flatten/Reshape"
+  op: "Flatten"
+  input: "conv4_3_norm_mbox_loc/BiasAdd"
+}
+node {
+  name: "conv4_3_norm_mbox_conf/Conv2D"
+  op: "Conv2D"
+  input: "conv4_3_norm/mul_1"
+  input: "conv4_3_norm_mbox_conf/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv4_3_norm_mbox_conf/BiasAdd"
+  op: "BiasAdd"
+  input: "conv4_3_norm_mbox_conf/Conv2D"
+  input: "conv4_3_norm_mbox_conf/bias"
+}
+node {
+  name: "flatten_6/Reshape"
+  op: "Flatten"
+  input: "conv4_3_norm_mbox_conf/BiasAdd"
+}
+node {
+  name: "Pad_1"
+  op: "SpaceToBatchND"
+  input: "Relu_4"
+  input: "SpaceToBatchND/block_shape"
+  input: "SpaceToBatchND_1/paddings"
+}
+node {
+  name: "layer_256_1_conv1/Conv2D"
+  op: "Conv2D"
+  input: "Pad_1"
+  input: "layer_256_1_conv1/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "VALID"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_256_1_bn2/FusedBatchNorm"
+  op: "BiasAdd"
+  input: "layer_256_1_conv1/Conv2D"
+  input: "layer_256_1_conv1/Conv2D_bn_offset"
+}
+node {
+  name: "BatchToSpaceND_1"
+  op: "BatchToSpaceND"
+  input: "layer_256_1_bn2/FusedBatchNorm"
+}
+node {
+  name: "layer_256_1_scale2/Mul"
+  op: "Mul"
+  input: "BatchToSpaceND_1"
+  input: "layer_256_1_scale2/mul"
+}
+node {
+  name: "layer_256_1_scale2/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_256_1_scale2/Mul"
+  input: "layer_256_1_scale2/add"
+}
+node {
+  name: "Relu_5"
+  op: "Relu"
+  input: "layer_256_1_scale2/BiasAdd"
+}
+node {
+  name: "layer_256_1_conv2/Conv2D"
+  op: "Conv2D"
+  input: "Relu_5"
+  input: "layer_256_1_conv2/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "add_2"
+  op: "Add"
+  input: "layer_256_1_conv2/Conv2D"
+  input: "layer_256_1_conv_expand/Conv2D"
+}
+node {
+  name: "layer_512_1_bn1/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "add_2"
+  input: "layer_512_1_bn1/gamma"
+  input: "layer_512_1_bn1/beta"
+  input: "layer_512_1_bn1/mean"
+  input: "layer_512_1_bn1/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "layer_512_1_scale1/Mul"
+  op: "Mul"
+  input: "layer_512_1_bn1/FusedBatchNorm"
+  input: "layer_512_1_scale1/mul"
+}
+node {
+  name: "layer_512_1_scale1/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_512_1_scale1/Mul"
+  input: "layer_512_1_scale1/add"
+}
+node {
+  name: "Relu_6"
+  op: "Relu"
+  input: "layer_512_1_scale1/BiasAdd"
+}
+node {
+  name: "layer_512_1_conv_expand_h/Conv2D"
+  op: "Conv2D"
+  input: "Relu_6"
+  input: "layer_512_1_conv_expand_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_512_1_conv1_h/Conv2D"
+  op: "Conv2D"
+  input: "Relu_6"
+  input: "layer_512_1_conv1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_512_1_bn2_h/FusedBatchNorm"
+  op: "BiasAdd"
+  input: "layer_512_1_conv1_h/Conv2D"
+  input: "layer_512_1_conv1_h/Conv2D_bn_offset"
+}
+node {
+  name: "layer_512_1_scale2_h/Mul"
+  op: "Mul"
+  input: "layer_512_1_bn2_h/FusedBatchNorm"
+  input: "layer_512_1_scale2_h/mul"
+}
+node {
+  name: "layer_512_1_scale2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "layer_512_1_scale2_h/Mul"
+  input: "layer_512_1_scale2_h/add"
+}
+node {
+  name: "Relu_7"
+  op: "Relu"
+  input: "layer_512_1_scale2_h/BiasAdd"
+}
+node {
+  name: "layer_512_1_conv2_h/convolution/SpaceToBatchND"
+  op: "SpaceToBatchND"
+  input: "Relu_7"
+  input: "layer_512_1_conv2_h/convolution/SpaceToBatchND/block_shape"
+  input: "layer_512_1_conv2_h/convolution/SpaceToBatchND/paddings"
+}
+node {
+  name: "layer_512_1_conv2_h/convolution"
+  op: "Conv2D"
+  input: "layer_512_1_conv2_h/convolution/SpaceToBatchND"
+  input: "layer_512_1_conv2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "VALID"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "layer_512_1_conv2_h/convolution/BatchToSpaceND"
+  op: "BatchToSpaceND"
+  input: "layer_512_1_conv2_h/convolution"
+  input: "layer_512_1_conv2_h/convolution/BatchToSpaceND/block_shape"
+  input: "layer_512_1_conv2_h/convolution/BatchToSpaceND/crops"
+}
+node {
+  name: "add_3"
+  op: "Add"
+  input: "layer_512_1_conv2_h/convolution/BatchToSpaceND"
+  input: "layer_512_1_conv_expand_h/Conv2D"
+}
+node {
+  name: "last_bn_h/FusedBatchNorm"
+  op: "FusedBatchNorm"
+  input: "add_3"
+  input: "last_bn_h/gamma"
+  input: "last_bn_h/beta"
+  input: "last_bn_h/mean"
+  input: "last_bn_h/std"
+  attr {
+    key: "epsilon"
+    value {
+      f: 1.00099996416e-05
+    }
+  }
+}
+node {
+  name: "last_scale_h/Mul"
+  op: "Mul"
+  input: "last_bn_h/FusedBatchNorm"
+  input: "last_scale_h/mul"
+}
+node {
+  name: "last_scale_h/BiasAdd"
+  op: "BiasAdd"
+  input: "last_scale_h/Mul"
+  input: "last_scale_h/add"
+}
+node {
+  name: "last_relu"
+  op: "Relu"
+  input: "last_scale_h/BiasAdd"
+}
+node {
+  name: "conv6_1_h/Conv2D"
+  op: "Conv2D"
+  input: "last_relu"
+  input: "conv6_1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv6_1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv6_1_h/Conv2D"
+  input: "conv6_1_h/bias"
+}
+node {
+  name: "conv6_1_h/Relu"
+  op: "Relu"
+  input: "conv6_1_h/BiasAdd"
+}
+node {
+  name: "conv6_2_h/Conv2D"
+  op: "Conv2D"
+  input: "conv6_1_h/Relu"
+  input: "conv6_2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 2
+        i: 2
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv6_2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv6_2_h/Conv2D"
+  input: "conv6_2_h/bias"
+}
+node {
+  name: "conv6_2_h/Relu"
+  op: "Relu"
+  input: "conv6_2_h/BiasAdd"
+}
+node {
+  name: "conv7_1_h/Conv2D"
+  op: "Conv2D"
+  input: "conv6_2_h/Relu"
+  input: "conv7_1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
+      s: "SAME"
+    }
+  }
+  attr {
+    key: "strides"
+    value {
+      list {
+        i: 1
+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+}
+node {
+  name: "conv7_1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv7_1_h/Conv2D"
+  input: "conv7_1_h/bias"
+}
+node {
+  name: "conv7_1_h/Relu"
+  op: "Relu"
+  input: "conv7_1_h/BiasAdd"
+}
+node {
+  name: "Pad_2"
+  op: "SpaceToBatchND"
+  input: "conv7_1_h/Relu"
+  input: "SpaceToBatchND/block_shape"
+  input: "SpaceToBatchND_1/paddings"
+}
+node {
+  name: "conv7_2_h/Conv2D"
+  op: "Conv2D"
+  input: "Pad_2"
+  input: "conv7_2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
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+        i: 1
+        i: 1
+        i: 1
+      }
+    }
+  }
+  attr {
+    key: "padding"
+    value {
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+    }
+  }
+  attr {
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+    value {
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+        i: 2
+        i: 2
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+    }
+  }
+}
+node {
+  name: "conv7_2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv7_2_h/Conv2D"
+  input: "conv7_2_h/bias"
+}
+node {
+  name: "BatchToSpaceND_2"
+  op: "BatchToSpaceND"
+  input: "conv7_2_h/BiasAdd"
+}
+node {
+  name: "conv7_2_h/Relu"
+  op: "Relu"
+  input: "BatchToSpaceND_2"
+}
+node {
+  name: "conv8_1_h/Conv2D"
+  op: "Conv2D"
+  input: "conv7_2_h/Relu"
+  input: "conv8_1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
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+        i: 1
+        i: 1
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+  }
+  attr {
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+  }
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv8_1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv8_1_h/Conv2D"
+  input: "conv8_1_h/bias"
+}
+node {
+  name: "conv8_1_h/Relu"
+  op: "Relu"
+  input: "conv8_1_h/BiasAdd"
+}
+node {
+  name: "conv8_2_h/Conv2D"
+  op: "Conv2D"
+  input: "conv8_1_h/Relu"
+  input: "conv8_2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
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+        i: 1
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+    }
+  }
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv8_2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv8_2_h/Conv2D"
+  input: "conv8_2_h/bias"
+}
+node {
+  name: "conv8_2_h/Relu"
+  op: "Relu"
+  input: "conv8_2_h/BiasAdd"
+}
+node {
+  name: "conv9_1_h/Conv2D"
+  op: "Conv2D"
+  input: "conv8_2_h/Relu"
+  input: "conv9_1_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
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+        i: 1
+        i: 1
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+    }
+  }
+  attr {
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+  }
+  attr {
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+    value {
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+        i: 1
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+      }
+    }
+  }
+}
+node {
+  name: "conv9_1_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv9_1_h/Conv2D"
+  input: "conv9_1_h/bias"
+}
+node {
+  name: "conv9_1_h/Relu"
+  op: "Relu"
+  input: "conv9_1_h/BiasAdd"
+}
+node {
+  name: "conv9_2_h/Conv2D"
+  op: "Conv2D"
+  input: "conv9_1_h/Relu"
+  input: "conv9_2_h/weights"
+  attr {
+    key: "dilations"
+    value {
+      list {
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+        i: 1
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+    }
+  }
+  attr {
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+    value {
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+  }
+  attr {
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+    value {
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+      }
+    }
+  }
+}
+node {
+  name: "conv9_2_h/BiasAdd"
+  op: "BiasAdd"
+  input: "conv9_2_h/Conv2D"
+  input: "conv9_2_h/bias"
+}
+node {
+  name: "conv9_2_h/Relu"
+  op: "Relu"
+  input: "conv9_2_h/BiasAdd"
+}
+node {
+  name: "conv9_2_mbox_loc/Conv2D"
+  op: "Conv2D"
+  input: "conv9_2_h/Relu"
+  input: "conv9_2_mbox_loc/weights"
+  attr {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv9_2_mbox_loc/BiasAdd"
+  op: "BiasAdd"
+  input: "conv9_2_mbox_loc/Conv2D"
+  input: "conv9_2_mbox_loc/bias"
+}
+node {
+  name: "flatten_5/Reshape"
+  op: "Flatten"
+  input: "conv9_2_mbox_loc/BiasAdd"
+}
+node {
+  name: "conv9_2_mbox_conf/Conv2D"
+  op: "Conv2D"
+  input: "conv9_2_h/Relu"
+  input: "conv9_2_mbox_conf/weights"
+  attr {
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+    value {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv9_2_mbox_conf/BiasAdd"
+  op: "BiasAdd"
+  input: "conv9_2_mbox_conf/Conv2D"
+  input: "conv9_2_mbox_conf/bias"
+}
+node {
+  name: "flatten_11/Reshape"
+  op: "Flatten"
+  input: "conv9_2_mbox_conf/BiasAdd"
+}
+node {
+  name: "conv8_2_mbox_loc/Conv2D"
+  op: "Conv2D"
+  input: "conv8_2_h/Relu"
+  input: "conv8_2_mbox_loc/weights"
+  attr {
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+    value {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv8_2_mbox_loc/BiasAdd"
+  op: "BiasAdd"
+  input: "conv8_2_mbox_loc/Conv2D"
+  input: "conv8_2_mbox_loc/bias"
+}
+node {
+  name: "flatten_4/Reshape"
+  op: "Flatten"
+  input: "conv8_2_mbox_loc/BiasAdd"
+}
+node {
+  name: "conv8_2_mbox_conf/Conv2D"
+  op: "Conv2D"
+  input: "conv8_2_h/Relu"
+  input: "conv8_2_mbox_conf/weights"
+  attr {
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+  attr {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv8_2_mbox_conf/BiasAdd"
+  op: "BiasAdd"
+  input: "conv8_2_mbox_conf/Conv2D"
+  input: "conv8_2_mbox_conf/bias"
+}
+node {
+  name: "flatten_10/Reshape"
+  op: "Flatten"
+  input: "conv8_2_mbox_conf/BiasAdd"
+}
+node {
+  name: "conv7_2_mbox_loc/Conv2D"
+  op: "Conv2D"
+  input: "conv7_2_h/Relu"
+  input: "conv7_2_mbox_loc/weights"
+  attr {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv7_2_mbox_loc/BiasAdd"
+  op: "BiasAdd"
+  input: "conv7_2_mbox_loc/Conv2D"
+  input: "conv7_2_mbox_loc/bias"
+}
+node {
+  name: "flatten_3/Reshape"
+  op: "Flatten"
+  input: "conv7_2_mbox_loc/BiasAdd"
+}
+node {
+  name: "conv7_2_mbox_conf/Conv2D"
+  op: "Conv2D"
+  input: "conv7_2_h/Relu"
+  input: "conv7_2_mbox_conf/weights"
+  attr {
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+    value {
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+  attr {
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+    value {
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+    }
+  }
+}
+node {
+  name: "conv7_2_mbox_conf/BiasAdd"
+  op: "BiasAdd"
+  input: "conv7_2_mbox_conf/Conv2D"
+  input: "conv7_2_mbox_conf/bias"
+}
+node {
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+  op: "Flatten"
+  input: "conv7_2_mbox_conf/BiasAdd"
+}
+node {
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+  op: "Conv2D"
+  input: "conv6_2_h/Relu"
+  input: "conv6_2_mbox_loc/weights"
+  attr {
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+    value {
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+    }
+  }
+}
+node {
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+  op: "BiasAdd"
+  input: "conv6_2_mbox_loc/Conv2D"
+  input: "conv6_2_mbox_loc/bias"
+}
+node {
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+  op: "Flatten"
+  input: "conv6_2_mbox_loc/BiasAdd"
+}
+node {
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+  op: "Conv2D"
+  input: "conv6_2_h/Relu"
+  input: "conv6_2_mbox_conf/weights"
+  attr {
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+  }
+}
+node {
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+  op: "BiasAdd"
+  input: "conv6_2_mbox_conf/Conv2D"
+  input: "conv6_2_mbox_conf/bias"
+}
+node {
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+  input: "conv6_2_mbox_conf/BiasAdd"
+}
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+  op: "Conv2D"
+  input: "last_relu"
+  input: "fc7_mbox_loc/weights"
+  attr {
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+    value {
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+}
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+  op: "BiasAdd"
+  input: "fc7_mbox_loc/Conv2D"
+  input: "fc7_mbox_loc/bias"
+}
+node {
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+  op: "Flatten"
+  input: "fc7_mbox_loc/BiasAdd"
+}
+node {
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+  op: "ConcatV2"
+  input: "flatten/Reshape"
+  input: "flatten_1/Reshape"
+  input: "flatten_2/Reshape"
+  input: "flatten_3/Reshape"
+  input: "flatten_4/Reshape"
+  input: "flatten_5/Reshape"
+  input: "mbox_loc/axis"
+}
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+  op: "Conv2D"
+  input: "last_relu"
+  input: "fc7_mbox_conf/weights"
+  attr {
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+}
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+  op: "BiasAdd"
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+  input: "fc7_mbox_conf/bias"
+}
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+  input: "fc7_mbox_conf/BiasAdd"
+}
+node {
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+  op: "ConcatV2"
+  input: "flatten_6/Reshape"
+  input: "flatten_7/Reshape"
+  input: "flatten_8/Reshape"
+  input: "flatten_9/Reshape"
+  input: "flatten_10/Reshape"
+  input: "flatten_11/Reshape"
+  input: "mbox_conf/axis"
+}
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+  op: "Reshape"
+  input: "mbox_conf"
+  input: "reshape_before_softmax"
+}
+node {
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+  op: "Softmax"
+  input: "mbox_conf_reshape"
+  attr {
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+    value {
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+}
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+  input: "mbox_conf_softmax"
+}
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+  op: "PriorBox"
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+  input: "data"
+  attr {
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+  attr {
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+  attr {
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+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
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+        float_val: 0.10000000149
+        float_val: 0.10000000149
+        float_val: 0.20000000298
+        float_val: 0.20000000298
+      }
+    }
+  }
+}
+node {
+  name: "PriorBox_4"
+  op: "PriorBox"
+  input: "conv8_2_h/Relu"
+  input: "data"
+  attr {
+    key: "aspect_ratio"
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+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
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+    key: "min_size"
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+  attr {
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+    value {
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+    }
+  }
+  attr {
+    key: "step"
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+    key: "variance"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 4
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+        float_val: 0.10000000149
+        float_val: 0.10000000149
+        float_val: 0.20000000298
+        float_val: 0.20000000298
+      }
+    }
+  }
+}
+node {
+  name: "PriorBox_5"
+  op: "PriorBox"
+  input: "conv9_2_h/Relu"
+  input: "data"
+  attr {
+    key: "aspect_ratio"
+    value {
+      tensor {
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+        tensor_shape {
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+  attr {
+    key: "min_size"
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+  attr {
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+    value {
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+  attr {
+    key: "step"
+    value {
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+        tensor_shape {
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+        float_val: 0.10000000149
+        float_val: 0.20000000298
+        float_val: 0.20000000298
+      }
+    }
+  }
+}
+node {
+  name: "mbox_priorbox"
+  op: "ConcatV2"
+  input: "PriorBox_0"
+  input: "PriorBox_1"
+  input: "PriorBox_2"
+  input: "PriorBox_3"
+  input: "PriorBox_4"
+  input: "PriorBox_5"
+  input: "mbox_loc/axis"
+}
+node {
+  name: "detection_out"
+  op: "DetectionOutput"
+  input: "mbox_loc"
+  input: "mbox_conf_flatten"
+  input: "mbox_priorbox"
+  attr {
+    key: "background_label_id"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "code_type"
+    value {
+      s: "CENTER_SIZE"
+    }
+  }
+  attr {
+    key: "confidence_threshold"
+    value {
+      f: 0.00999999977648
+    }
+  }
+  attr {
+    key: "keep_top_k"
+    value {
+      i: 200
+    }
+  }
+  attr {
+    key: "nms_threshold"
+    value {
+      f: 0.449999988079
+    }
+  }
+  attr {
+    key: "num_classes"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "share_location"
+    value {
+      b: true
+    }
+  }
+  attr {
+    key: "top_k"
+    value {
+      i: 400
+    }
+  }
+}
+node {
+  name: "reshape_before_softmax"
+  op: "Const"
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_INT32
+        tensor_shape {
+          dim {
+            size: 3
+          }
+        }
+        int_val: 0
+        int_val: -1
+        int_val: 2
+      }
+    }
+  }
+}
+library {
+}

emotion_recognition/models/opencv_face_detector_uint8.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c71d752ef2cbf2f457ac82fdd580fcb2522fd04c5efdaed18eb6d9e2843fbed
+size 2727750

main.py

@@ -0,0 +1,276 @@
+from flask import Flask, jsonify, request
+import os
+# from "./emotion_recognition/ai_model_photo" import ai
+from emotion_recognition import ai_model_photo
+# from palm_response import getResponse, init_connect
+from flask_mysqldb import MySQL
+import base64
+import google.generativeai as genai
+
+app = Flask(__name__)
+
+app.config['MYSQL_HOST'] = 'localhost'
+app.config['MYSQL_USER'] = 'root'
+app.config['MYSQL_PASSWORD'] = ''
+app.config['MYSQL_DB'] = 'wanees_app'
+
+mysql = MySQL(app)
+
+
+def add_padding(base64_str):
+    missing_padding = len(base64_str) % 4
+    if missing_padding != 0:
+        base64_str += '=' * (4 - missing_padding)
+    return base64_str
+
+
+@app.route('/api/chat/gemini', methods=['POST'])
+def receive_chat():
+    try:
+        data = request.get_json()
+        prompt = data['message']
+        print(data)
+        print('promp is : ',prompt)
+        genai.configure(api_key="AIzaSyB7bKpGzkT_f32d-Xv4tb9zIBtb8vJUCCs")
+        model = genai.GenerativeModel('gemini-pro') 
+        response = model.generate_content(prompt)
+        print(response.text)
+        
+        generated_text = response.text
+        print('generated_text: ',generated_text)
+        return jsonify({"response": generated_text}), 200
+    except Exception as e:
+        print('the catch is:',str(e))
+        return jsonify({"error": str(e)})
+
+# openai.api_key = "sk-proj-QtkCbMdxHjgMAyughGjgT3BlbkFJuoyNuhK2zO3uKm1Zc5S6"
+
+# @backoff.on_exception(backoff.expo, RateLimitError, max_tries=8)
+# def completions_with_backoff(client, messages):
+#     response = client.chat.completions.create(
+#         model="gpt-3.5-turbo",
+#         messages=messages,
+#         temperature=0,
+#     )
+#     return response
+
+
+# @app.route('/api/chat/gpt', methods=['POST'])
+# def receive_message():
+#     try:
+#         data = request.get_json()
+#         prompt = data.get('prompt', 'Hello')
+#         print(data)
+#         print('promp is : ',prompt)
+#         # client = OpenAI()
+#         messages = [{"role": "user", "content": prompt}]
+#         client = OpenAI(
+#         # This is the default and can be omitted
+#         api_key="sk-proj-EPQes2YgSJEx1e6EwDarT3BlbkFJrEtY36IDssJKi6VkVP7v"
+#         )
+#         #
+#         response = client.chat.completions.create(
+#         model="gpt-3.5-turbo",
+#         messages=messages,
+#         temperature=0,
+#         )
+        
+#         # response = openai.Completion.create(
+#         #     engine="text-davinci-003",
+#         #     prompt=prompt,
+#         #     max_tokens=251,
+#         #     temperature=0,
+#         #     top_p=1
+#         # )
+#         print('response is: ',response.choices[0].message["content"])
+        
+#         generated_text = response.choices[0].message["content"]
+#         print('generated_text: ',generated_text)
+#         return jsonify({"response": generated_text}), 200
+#     except Exception as e:
+#         print('the catch is:',str(e))
+#         return jsonify({"error": str(e)})
+
+
+@app.route('/api/photo', methods=['POST'])
+def receive_photo():
+    global photo_path
+    photo_data = request.files['photo']
+    photo_path = os.path.join('.', photo_data.filename)
+    photo_data.save(photo_path)
+    res = ai_model_photo.ai(photo_path)
+    print(photo_path)
+    return jsonify({'message': res}), 200
+    # return Response(status=200, mimetype='application/json')
+
+
+@app.route('/api/facedetect', methods=['POST'])
+def AddPhoto():
+    name = request.json['name']
+    email = request.json['email']
+    # mobile = request.json['mobile']
+    img64 = request.json['image']
+    img64_padded = add_padding(img64)
+    imageBinary = base64.b64decode(img64_padded)
+
+    imgdetect = request.json['imagedetect']
+    imgdetect_padded = add_padding(imgdetect)
+    imageBinarydetect = base64.b64decode(imgdetect_padded)
+    photo_path = os.path.join('.', imageBinarydetect.filename)
+    imageBinary.save(photo_path)
+    res = ai_model_photo.ai(photo_path)
+    print(photo_path)
+
+# def receive_photo():
+#     global photo_path
+#     photo_data = request.files['photo']
+#     photo_path = os.path.join('.', photo_data.filename)
+#     photo_data.save(photo_path)
+#     res = ai_model_photo.ai(photo_path)
+#     print(photo_path)
+    return jsonify({'message': res}), 200
+
+
+# @app.route('/api/chat/palm', methods=['POST'])
+# def receive_message():
+#     message ={
+#        "message" : request.json['message']
+#     }
+#     response_palm = getResponse(message["message"])
+#     print("res palm =====", response_palm)
+#     return jsonify({'message': response_palm}), 200
+
+
+@app.route('/add', methods=['POST'])
+def Add():
+    name = request.json['name']
+    email = request.json['email']
+    mobile = request.json['mobile']
+    mobile_emergency = request.json['mobile_emergency']
+    age = request.json['age']
+    gender = request.json['gender']
+    location = request.json['location']
+    img64 = request.json['image']
+    user_disease = request.json['user_disease']
+    user_medicine = request.json['user_medicine']
+
+    img64_padded = add_padding(img64)
+    imageBinary = base64.b64decode(img64_padded)
+
+    cur = mysql.connection.cursor()
+    cur.execute("""
+                INSERT INTO users
+                (name, email ,mobile, mobile_emergency, age, gender, location, image)
+                VALUES (%s,%s,%s,%s,%s,%s,%s,%s) 
+                """, (name, email, mobile, mobile_emergency, age, gender, location, imageBinary))
+    mysql.connection.commit()
+    cur.execute("""
+                SELECT * FROM users 
+                WHERE name=%s AND email=%s 
+                """, (name, email))
+    data = cur.fetchall()
+    id = data[0][0]
+    for disease in user_disease:
+        print(disease)
+        cur.execute("""INSERT INTO user_disease 
+                    (user_id, disease_name) 
+                    VALUES (%s, %s)
+                    """, (id, disease))
+    for medicine in user_medicine:
+        img64 = medicine['medicine_image']
+        img64_padded = add_padding(img64)
+        imageBinary = base64.b64decode(img64_padded)
+        cur.execute("""INSERT INTO user_medicine 
+                    (user_id, medicine_name,medicine_dose ,medicine_image) 
+                    VALUES (%s, %s, %s, %s)
+                    """, (id, medicine['medicine_name'], medicine['medicine_dose'], medicine['medicine_image']))
+    mysql.connection.commit()
+    cur.close()
+    return jsonify({"message": "done"}), 200
+
+
+@app.route('/delete/<string:email>', methods=['DELETE'])
+def Delete(email):
+    cur = mysql.connection.cursor()
+    cur.execute("""
+                SELECT id FROM users 
+                WHERE email=%s 
+                """, (email, ))
+    data = cur.fetchall()
+    id = data[0][0]
+    cur.execute("DELETE FROM user_disease WHERE user_id=%s", (id,))
+    cur.execute("DELETE FROM user_medicine WHERE user_id=%s", (id,))
+    cur.execute("DELETE FROM users WHERE id=%s", (id,))
+    mysql.connection.commit()
+    return jsonify({"message": "done"}), 200
+
+
+@app.route('/update/<int:id>',  methods=['PUT'])
+def Update(id):
+    name = request.json['name']
+    email = request.json['email']
+    mobile = request.json['mobile']
+    mobile_emergency = request.json['mobile_emergency']
+    age = request.json['age']
+    gender = request.json['gender']
+    location = request.json['location']
+    img64 = request.json['image']
+    user_disease = request.json['user_disease']
+    user_medicine = request.json['user_medicine']
+
+    img64_padded = add_padding(img64)
+    imageBinary = base64.b64decode(img64_padded)
+
+    cur = mysql.connection.cursor()
+    cur.execute("""
+            UPDATE users 
+            SET name=%s,email=%s, mobile=%s ,mobile_emergency=%s ,age=%s ,gender=%s ,location=%s ,image=%s
+            WHERE id=%s 
+            """, (name, email, mobile, mobile_emergency, age, gender, location, imageBinary, id))
+    cur.execute("DELETE FROM user_disease WHERE user_id=%s", (id,))
+    cur.execute("DELETE FROM user_medicine WHERE user_id=%s", (id,))
+
+    for disease in user_disease:
+        cur.execute("""INSERT INTO user_disease 
+                    (user_id, disease_name) 
+                    VALUES (%s, %s)
+                    """, (id, disease['disease_name']))
+    for medicine in user_medicine:
+        cur.execute("""INSERT INTO user_medicine 
+                    (user_id, medicine_name, medicine_image) 
+                    VALUES (%s, %s, %s)
+                    """, (id, medicine['medicine_name'], medicine['medicine_image']))
+    mysql.connection.commit()
+    return jsonify({"message": "done"}), 200
+
+
+@app.route('/select/<int:id>', methods=['GET'])
+def Select(id):
+    cur = mysql.connection.cursor()
+    cur.execute("SELECT * FROM users WHERE id=%s", (id,))
+    table1 = cur.fetchall()
+    cur.execute("SELECT * FROM user_disease WHERE user_id=%s", (id,))
+    table2 = cur.fetchall()
+    cur.execute("SELECT * FROM user_medicine WHERE user_id=%s", (id,))
+    table3 = cur.fetchall()
+    cur.close()
+    user_data = {
+        "name": table1[0][1],
+        "email": table1[0][2],
+        "mobile": table1[0][3],
+        "mobile_emergency": table1[0][4],
+        "age": table1[0][5],
+        "gender": table1[0][6],
+        "location": table1[0][7],
+        "image": base64.b64encode(table1[0][8]).decode('utf-8'),
+        "user_disease": [{"disease_name": disease[1]} for disease in table2],
+        "user_medicine": [{"medicine_name": medicine[1], "medicine_image": base64.b64encode(medicine[3]).decode('utf-8')} for medicine in table3]
+    }
+    return jsonify(user_data), 200
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)
+    # init_connect()
+    # app.run(host='192.168.137.241', port=5000, debug=True) # 192.168.1.114 http://192.168.137.241:5000  192.168.1.122
+