Update app.py

app.py

@@ -1,82 +1,4 @@
-# import gradio as gr
-# import os
-# import cv2
-# import numpy as np
-# import imutils
-# from keras.preprocessing.image import img_to_array
-# from keras.models import load_model
-
-# # Load the pre-trained models and define parameters
-# detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
-# emotion_model_path = 'model4_0.83/model4_entire_model.h5'
-# face_detection = cv2.CascadeClassifier(detection_model_path)
-# emotion_classifier = load_model(emotion_model_path, compile=False)
-# EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
-
-
-# # face_detector_mtcnn = MTCNN()
-# classifier = load_model(emotion_model_path)
-
-# def predict_emotion(frame):
-#     frame = imutils.resize(frame, width=300)
-#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-#     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
-#                                             minNeighbors=5, minSize=(30, 30),
-#                                             flags=cv2.CASCADE_SCALE_IMAGE)
-
-#     for (fX, fY, fW, fH) in faces:
-#         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
-#         # the ROI for classification via the CNN
-#         roi = gray[fY:fY + fH, fX:fX + fW]
-#         roi = cv2.resize(roi, (48, 48))
-#         roi = roi.astype("float") / 255.0
-#         roi = img_to_array(roi)
-#         roi = np.expand_dims(roi, axis=0)
-
-#         preds = emotion_classifier.predict(roi)[0]
-#         label = EMOTIONS[preds.argmax()]
-
-#         # Overlay a box over the detected face
-#         cv2.putText(frame, label, (fX, fY - 10),
-#                     cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1, cv2.LINE_AA)
-#         cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
-#                       (238, 164, 64), 2)
-
-#     return frame
-
-
-
-
-
-# demo = gr.Interface(
-#     fn = predict_emotion,
-#     inputs = gr.Image(type="numpy"),
-#     outputs = gr.Image(),
-#     # gr.components.Image(label="Predicted Emotion"),
-#     # gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
-#     #flagging_options=["blurry", "incorrect", "other"],
-#     examples = [
-        
-#         os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
-#         os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
-     
-#     ],
-#     title = "How are you feeling?",
-#     theme = "shivi/calm_seafoam"
-# )
-    
-
-
-# if __name__ == "__main__":
-#     demo.launch()
-    
-######################################################################################################################################################
-import gradio as gr
+mport gradio as gr
 import os
 import cv2
 import numpy as np
@@ -91,21 +13,17 @@ face_detection = cv2.CascadeClassifier(detection_model_path)
 emotion_classifier = load_model(emotion_model_path, compile=False)
 EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
 
-def predict_emotion(frame):
-    if isinstance(frame, np.ndarray):
-        # Image input
-        frame = predict_image(frame)
-    else:
-        # Video frame input
-        frame = predict_video_frame(frame.read())  # Convert NamedString to bytes
-    return frame
 
-def predict_image(frame):
+# face_detector_mtcnn = MTCNN()
+classifier = load_model(emotion_model_path)
+
+def predict_emotion(frame):
     frame = imutils.resize(frame, width=300)
     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
                                             minNeighbors=5, minSize=(30, 30),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
+
     for (fX, fY, fW, fH) in faces:
         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
         # the ROI for classification via the CNN
@@ -123,28 +41,36 @@ def predict_image(frame):
                     cv2.FONT_HERSHEY_DUPLEX, 0.5, (238, 164, 64), 1, cv2.LINE_AA)
         cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
                       (238, 164, 64), 2)
+
     return frame
 
-def predict_video_frame(frame):
-    return predict_image(frame)
+
+
+
 
 demo = gr.Interface(
     fn = predict_emotion,
-    inputs = [gr.components.File()],  # Corrected input components
-    outputs = gr.components.File(),
+    inputs = gr.Image(type="numpy"),
+    outputs = gr.Image(),
+    # gr.components.Image(label="Predicted Emotion"),
+    # gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
+    #flagging_options=["blurry", "incorrect", "other"],
     examples = [
-        [os.path.join(os.path.dirname(__file__), "images/chandler.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/janice.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/joey.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/ross.jpeg")],
-        [os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")],
-        [open(os.path.join(os.path.dirname(__file__), "images/joey_shocked.mp4"), "rb")]  # Open file in binary mode
+        
+        os.path.join(os.path.dirname(__file__), "images/chandler.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
+        os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
+     
     ],
     title = "How are you feeling?",
     theme = "shivi/calm_seafoam"
 )
+    
+
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()