Update app.py

app.py

@@ -1,30 +1,23 @@
 import gradio as gr
 import os
-import keras
-from keras.preprocessing.image import img_to_array
-import imutils
 import cv2
-from keras.models import load_model
 import numpy as np
+import imutils
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
 
-# parameters for loading data and images
+# Load the pre-trained models and define parameters
 detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
-# emotion_model_path = 'model2/model2_entire_model.h5'
 emotion_model_path = 'model_2_aug_nocall_BEST/model_2_aug_nocall_entire_model.h5'
-
-
-
-# hyper-parameters for bounding boxes shape
-# loading models
 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']
-
+EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']
 
+# Function to predict emotions from a frame
 def predict(frame_or_path):
-    # if isinstance(frame_or_path, np.ndarray):  # If input is a webcam frame
-    #     frame = imutils.resize(frame_or_path, width=300)
-    # if:  # If input is a file path
+    if isinstance(frame_or_path, np.ndarray):  # If input is a webcam frame
+        frame = imutils.resize(frame_or_path, width=300)
+    else:  # If input is a file path
         frame = cv2.imread(frame_or_path)
         if frame is None:
             return None, "Error: Unable to read image or video."
@@ -48,65 +41,15 @@ def predict(frame_or_path):
                       (238, 164, 64), 2)
         return frame, {emotion: float(prob) for emotion, prob in zip(EMOTIONS, preds)}
 
+# Define input and output components for Gradio
+image_input = gr.inputs.Image(sources=["webcam", "upload"], label="Your face")
+image_output = gr.outputs.Image(label="Predicted Emotion")
+label_output = gr.outputs.Label(num_top_classes=2, label="Top 2 Probabilities")
 
-
-# # Define Gradio input and output components
-# image_input = gr.components.Image(type='numpy', label="Upload Image or Video")
-# image_output = gr.components.Image(label="Predicted Emotion")
-# label_output = gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
-
-# inp = [
-#         gr.components.Image(sources="webcam", label="Your face"),
-#         gr.components.File(label="Upload Image or Video")
-#       ]
-# out = [
-#         gr.components.Image(label="Predicted Emotion"),
-#         gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
-#         ]
-
-
-inp = [
-        gr.components.Image(sources="webcam", label="Your face"),
-        gr.components.File(label="Upload Image or Video")
-      ]
-out = [
-        gr.components.Image(label="Predicted Emotion"),
-        gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
-      ]
-
-example_images = [
-    [
-        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")
-    ]
-]
-# example_images = [
-#     ["images/chandler.jpeg"],
-#     ["images/janice.jpg"],
-#     ["images/joey.jpg"],
-#     ["images/phoebe.jpg"],
-#     ["images/rachel_monica.jpg"],
-#     ["images/ross.jpg"],
-#     ["images/gunther.jpg"]
-# ]
-
+# Launch the Gradio interface
 title = "Facial Emotion Recognition"
-description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
-              " you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
+description = "How well can this model predict your emotions? Take a picture with your webcam, or upload an image, and it will guess if you are happy, sad, angry, disgusted, scared, surprised, or neutral."
 thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
 
-# gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail,
-#              description=description).launch(inbrowser=True)
-gr.Interface(fn=predict, inputs=inp, outputs=out,
-            examples=example_images,title=title, thumbnail=thumbnail).launch()
-
-
-
-# # Launch Gradio interface
-# gr.Interface(fn=predict, inputs=image_input, outputs=[image_output, label_output],
-             # title="Facial Emotion Recognition", description="How well can this model predict your emotions?").launch()
+gr.Interface(fn=predict, inputs=image_input, outputs=[image_output, label_output],
+            title=title, description=description, thumbnail=thumbnail).launch()