Create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the labels
+def load_labels(filename):
+    with open(filename, "r") as file:
+        labels = [line.strip().split(" ", 1)[1] for line in file.readlines()]
+    return labels
+
+# Load the model
+def load_model():
+    return tf.keras.models.load_model("model/keras_model.h5")
+
+# Prediction function
+def predict_expression(image):
+    model = load_model()
+    img = tf.keras.preprocessing.image.load_img(image, target_size=(48, 48), color_mode="grayscale")
+    img_array = tf.keras.preprocessing.image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    predictions = model.predict(img_array)
+    label_index = np.argmax(predictions)
+    confidence = predictions[0][label_index] * 100
+    return label_index, confidence
+
+# Safety management logic
+def get_safety_measures(label_index, confidence):
+    safety_responses = [
+        "Alert nearby people and take immediate action if you sense danger.",
+        "Show calmness and maintain distance from the source of discomfort.",
+        "Encourage communication and provide support to reduce fear.",
+        "No action needed, but stay vigilant to avoid any potential threats.",
+        "Check for signs of discomfort or untruthfulness. Handle the situation carefully.",
+        "Provide emotional support and ensure safety from external threats.",
+        "Reassure the person and guide them to a safe environment."
+    ]
+    safety_action = safety_responses[label_index]
+    return f"Expression: {labels[label_index]} (Confidence: {confidence:.2f}%)\n\nRecommended Action:\n{safety_action}"
+
+# Load labels from file
+labels = load_labels("labels.txt")
+
+# Gradio interface
+def analyze_expression(image):
+    label_index, confidence = predict_expression(image)
+    result = get_safety_measures(label_index, confidence)
+    return result
+
+# Define the Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("# Woman Safety Management System 🌸\nUpload an image to analyze expressions and take appropriate safety actions.")
+    with gr.Row():
+        image_input = gr.Image(type="filepath", label="Upload Image")
+        analyze_btn = gr.Button("Analyze")
+    result_output = gr.Textbox(label="Analysis Result", lines=8)
+    
+    analyze_btn.click(analyze_expression, inputs=image_input, outputs=result_output)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()