Update app.py

app.py

@@ -1,44 +1,94 @@
 import gradio as gr
-from tensorflow.keras.models import load_model
-from PIL import Image, ImageOps
+import tensorflow as tf
 import numpy as np
 
-# Load the trained model
-model = load_model("model/keras_model.h5", compile=False)
-
-# Load the emotion labels
-emotion_labels = open("model/labels.txt", "r").readlines()
-
-# Define the prediction function
-def predict_emotion(image):
-    # Convert image to RGB and preprocess
-    image = image.convert("RGB")
-    size = (224, 224)
-    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
-    image_array = np.asarray(image)
-
-    # Normalize the image
-    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
-    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-    data[0] = normalized_image_array
-
-    # Predict the emotion
-    prediction = model.predict(data)
-    index = np.argmax(prediction)
-    emotion = emotion_labels[index].strip()
-    confidence_score = prediction[0][index]
-
-    # Return the predicted emotion and confidence score
-    return f"Emotion: {emotion}, Confidence Score: {confidence_score:.2f}"
-
-# Define the Gradio interface
-interface = gr.Interface(
-    fn=predict_emotion,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Emotion Detection",
-    description="Upload an image to detect the emotion.",
-)
-
-# Launch the interface
-interface.launch()
+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/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()