In c

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.h5
+*.json
+*.ipynb
+gradio_cached_examples/

requirements.txt

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+numpy==1.26.4
+opencv-python==4.9.0.80
+tensorflow==2.16.1
+keras==3.3.3
+pandas==2.2.2
+matplotlib ==3.8.2
+tqdm==4.66.1
+flask
+pillow
+gradio

secmain.py

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+import cv2
+import numpy as np
+import gradio as gr
+from keras._tf_keras.keras.models import model_from_json
+from tensorflow import keras  # Assuming TensorFlow backend for your model
+
+# Function to load the model (assuming saved as HDF5)
+def load_model():
+    json_file = open("emotiondetector.json", "r")
+    model_json = json_file.read()
+    json_file.close()
+    model = model_from_json(model_json)
+    model.load_weights("emotiondetectorweights.weights.h5")
+    return model
+  
+
+# Function to extract features from an image
+def extract_features(image):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # Convert to grayscale
+    image = cv2.resize(image, (48, 48))  # Resize to model input size
+    feature = np.array(image).reshape(1, 48, 48, 1)  # Reshape for model input
+    return feature / 255.0  # Normalize pixel values
+
+# Function to detect emotions in an image
+def detect_emotion(image):
+    model = load_model()  # Load the model on each inference
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    faces = face_cascade.detectMultiScale(image, 1.3, 5)
+
+    if len(faces) > 0:
+        (p, q, r, s) = faces[0]
+        face_image = image[q:q + s, p:p + r]
+        face_features = extract_features(face_image)
+        prediction = model.predict(face_features)
+        prediction_label = labels[prediction.argmax()]
+        return prediction_label
+    else:
+        return "No face detected"  # Handle no face case
+
+# Define emotion labels (modify based on your model's output)
+labels = {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy',
+          4: 'neutral', 5: 'sad', 6: 'surprise'}
+
+# Gradio interface definition
+interface = gr.Interface(
+    fn=detect_emotion,
+    inputs="image",
+    outputs="text",
+    description="Emotion Detection with Your Model",
+    allow_flagging=True  # Allow reporting issues
+)
+
+# Launch the Gradio app on Hugging Face Spaces
+interface.launch(share=True)  # Set "share=True" for public deployment
+