Upload folder using huggingface_hub

Assignment.py

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+import cv2
+import numpy as np
+from keras.models import load_model
+import gradio as gr
+
+# Load pre-trained model
+model = load_model("emotion_detection_model.h5")
+
+# Load OpenCV's pre-trained face detector
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)
+
+# Define emotion labels
+EMOTIONS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+
+
+# Function to detect and display emotions on faces
+def detect_emotions(image):
+    frame = image.copy()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    for x, y, w, h in faces:
+        face_roi = gray[y : y + h, x : x + w]
+        resized_roi = cv2.resize(face_roi, (48, 48))
+        normalized_roi = resized_roi / 255.0
+        reshaped_roi = normalized_roi.reshape(-1, 48, 48, 1)
+
+        # Predict emotions
+        predictions = model.predict(reshaped_roi)
+        emotion_label = EMOTIONS[np.argmax(predictions)]
+
+        # Display emotion label on face
+        cv2.putText(
+            frame,
+            emotion_label,
+            (x, y - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.9,
+            (36, 255, 12),
+            2,
+        )
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+    return frame
+
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=detect_emotions,
+    inputs="image",
+    outputs="image",
+    title="Emotion Detection",
+    description="Upload an Image to Detect Emotions in Faces",
+)
+
+# Run Gradio app
+iface.launch(share=True)

README.md

@@ -1,12 +1,6 @@
 ---
 title: DD
-emoji: 🏢
-colorFrom: green
-colorTo: indigo
+app_file: Assignment.py
 sdk: gradio
 sdk_version: 4.25.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

emotion_detection_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ccfaeabadde5ff6fa9b82e01a12f9d13e96ada7b17798ead57d247d81941993f
+size 17366976