Update app.py

app.py

@@ -1,94 +1,94 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-
-def load_labels(filename):
-    with open(filename, "r") as file:
-        return [line.strip() for line in file.readlines()]
-
-# Function to load the trained model and predict emotion
-def model_prediction(test_image):
-    model = tf.keras.models.load_model("model/emotion_detection_model.keras")
-    image = tf.keras.preprocessing.image.load_img(test_image, target_size=(48, 48), color_mode="grayscale")
-    input_arr = tf.keras.preprocessing.image.img_to_array(image) / 255.0
-    input_arr = np.expand_dims(input_arr, axis=0)
-    predictions = model.predict(input_arr)
-    return predictions
-
-labels = load_labels("labels.txt")
-
-def get_color(value):
-    if value >= 75:
-        return "label_red"
-    elif value >= 50:
-        return "label_yellow"
-    else:
-        return "label_green"
-
-def create_colored_box(value):
-    color_class = get_color(value)
-    return gr.Label(value=f"{value:.2f}%", elem_id=color_class)
-
-def predict_emotion(test_image):
-    predictions = model_prediction(test_image)
-    emotion_index = np.argmax(predictions)
-    emotion_probability = predictions[0][emotion_index] * 100
-    emotion_name = labels[emotion_index]
-
-    # Predict level of danger based on emotion
-    danger_level = 0
-    if emotion_name == "sad":
-        danger_level = min(100, emotion_probability * 1.2)
-    elif emotion_name == "fearful":
-        danger_level = min(100, emotion_probability * 1.5)
-    elif emotion_name == "fake_expression":
-        danger_level = 70  # Example fixed percentage for fake expression
-
-    return (
-        emotion_name, 
-        create_colored_box(emotion_probability), 
-        create_colored_box(danger_level)
-    )
-
-def home():
-    return (
-        "# Welcome to Emotion Analysis for Women Safety! 🌟\n\n"
-        "Our mission is to analyze facial emotions and assess the level of potential danger based on detected expressions. "
-        "This tool is designed to assist in identifying situations where immediate action might be needed to ensure safety.\n\n"
-        "### Features\n"
-        "- Detect facial emotions with high accuracy.\n"
-        "- Predict potential danger levels based on emotion analysis.\n"
-        "- User-friendly interface for seamless experience.\n\n"
-        "### How to Use\n"
-        "1. Upload an image showing the face.\n"
-        "2. View detected emotions and danger percentage.\n"
-        "3. Take necessary actions based on insights."
-    )
-
-with gr.Blocks(css=""" 
-.label_green { background-color: green; color: white; }
-.label_yellow { background-color: yellow; color: black; }
-.label_red { background-color: red; color: white; }
-""") as demo:
-    gr.Markdown("# Emotion Analysis for Women Safety")
-    gr.Image("logo.webp")
-    with gr.Tabs():
-        with gr.TabItem("Home"):
-            gr.Markdown(home())
-        with gr.TabItem("Emotion Analysis"):
-            gr.Markdown("## Upload a facial image for emotion analysis:")
-            test_image = gr.Image(type="filepath", label="Upload Image")
-
-            with gr.Row():
-                predict_btn = gr.Button("Analyze Emotion")
-            emotion_label = gr.Label(label="Detected Emotion")
-            probability_label = gr.Label(label="Emotion Probability")
-            danger_label = gr.Label(label="Danger Level (%)")
-
-            predict_btn.click(predict_emotion, inputs=test_image, outputs=[
-                emotion_label, 
-                probability_label, 
-                danger_label
-            ])
-
-demo.launch()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+def load_labels(filename):
+    with open(filename, "r") as file:
+        return [line.strip() for line in file.readlines()]
+
+# Function to load the trained model and predict emotion
+def model_prediction(test_image):
+    model = tf.keras.models.load_model("keras_model.h5")
+    image = tf.keras.preprocessing.image.load_img(test_image, target_size=(48, 48), color_mode="grayscale")
+    input_arr = tf.keras.preprocessing.image.img_to_array(image) / 255.0
+    input_arr = np.expand_dims(input_arr, axis=0)
+    predictions = model.predict(input_arr)
+    return predictions
+
+labels = load_labels("labels.txt")
+
+def get_color(value):
+    if value >= 75:
+        return "label_red"
+    elif value >= 50:
+        return "label_yellow"
+    else:
+        return "label_green"
+
+def create_colored_box(value):
+    color_class = get_color(value)
+    return gr.Label(value=f"{value:.2f}%", elem_id=color_class)
+
+def predict_emotion(test_image):
+    predictions = model_prediction(test_image)
+    emotion_index = np.argmax(predictions)
+    emotion_probability = predictions[0][emotion_index] * 100
+    emotion_name = labels[emotion_index]
+
+    # Predict level of danger based on emotion
+    danger_level = 0
+    if emotion_name == "sad":
+        danger_level = min(100, emotion_probability * 1.2)
+    elif emotion_name == "fearful":
+        danger_level = min(100, emotion_probability * 1.5)
+    elif emotion_name == "fake_expression":
+        danger_level = 70  # Example fixed percentage for fake expression
+
+    return (
+        emotion_name, 
+        create_colored_box(emotion_probability), 
+        create_colored_box(danger_level)
+    )
+
+def home():
+    return (
+        "# Welcome to Emotion Analysis for Women Safety! 🌟\n\n"
+        "Our mission is to analyze facial emotions and assess the level of potential danger based on detected expressions. "
+        "This tool is designed to assist in identifying situations where immediate action might be needed to ensure safety.\n\n"
+        "### Features\n"
+        "- Detect facial emotions with high accuracy.\n"
+        "- Predict potential danger levels based on emotion analysis.\n"
+        "- User-friendly interface for seamless experience.\n\n"
+        "### How to Use\n"
+        "1. Upload an image showing the face.\n"
+        "2. View detected emotions and danger percentage.\n"
+        "3. Take necessary actions based on insights."
+    )
+
+with gr.Blocks(css=""" 
+.label_green { background-color: green; color: white; }
+.label_yellow { background-color: yellow; color: black; }
+.label_red { background-color: red; color: white; }
+""") as demo:
+    gr.Markdown("# Emotion Analysis for Women Safety")
+    gr.Image("logo.webp")
+    with gr.Tabs():
+        with gr.TabItem("Home"):
+            gr.Markdown(home())
+        with gr.TabItem("Emotion Analysis"):
+            gr.Markdown("## Upload a facial image for emotion analysis:")
+            test_image = gr.Image(type="filepath", label="Upload Image")
+
+            with gr.Row():
+                predict_btn = gr.Button("Analyze Emotion")
+            emotion_label = gr.Label(label="Detected Emotion")
+            probability_label = gr.Label(label="Emotion Probability")
+            danger_label = gr.Label(label="Danger Level (%)")
+
+            predict_btn.click(predict_emotion, inputs=test_image, outputs=[
+                emotion_label, 
+                probability_label, 
+                danger_label
+            ])
+
+demo.launch()