added requirements and application file

app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+from transformers import ViTImageProcessor, ViTForImageClassification
+from PIL import Image
+import numpy as np
+
+MODEL_NAME = "mo-thecreator/vit-Facial-Expression-Recognition"
+EMOTIONS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+
+# Load model and processor
+processor = ViTImageProcessor.from_pretrained(MODEL_NAME)
+model = ViTForImageClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+def analyze_emotion(image):
+    if image is None:
+        return "Upload an image", None
+
+    # Preprocess
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=-1)[0].numpy()
+
+    # Get top emotion
+    top_idx = np.argmax(probs)
+    top_emotion = EMOTIONS[top_idx]
+
+    # Prepare bar chart
+    chart_data = {"emotion": EMOTIONS, "confidence": probs.tolist()}
+
+    result_text = f"Predicted Emotion: {top_emotion} ({probs[top_idx]*100:.1f}%)"
+
+    return result_text, chart_data
+
+# Build Gradio interface
+demo = gr.Interface(
+    fn=analyze_emotion,
+    inputs=gr.Image(type="pil", label="Upload Facial Image"),
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.BarPlot(x="emotion", y="confidence", y_lim=[0,1], label="Confidence")
+    ],
+    title="Facial Expression Recognition (ViT)",
+    description="Upload a facial image and detect emotions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral) using a Vision Transformer."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+gradio>=4.0.0
+transformers>=4.36.0
+torch>=2.0.0
+pillow>=8.3.2