Create zero_shot_emotion.py

zero_shot_emotion.py

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+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+import torch
+import gradio as gr
+
+# Modell laden
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+model.eval()
+
+# Deine Emotion Labels
+emotion_labels = [
+    "a happy person",
+    "a sad person",
+    "an angry person",
+    "a surprised person",
+    "a fearful person",
+    "a disgusted person",
+    "a neutral person",
+    "a contemptuous person",
+    "an unknown emotion"
+]
+
+# Funktion
+def zero_shot_predict(image):
+    image = image.convert("RGB")
+
+    inputs = processor(
+        text=emotion_labels,
+        images=image,
+        return_tensors="pt",
+        padding=True
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits_per_image = outputs.logits_per_image  # Bild-Text Ähnlichkeiten
+    probs = logits_per_image.softmax(dim=1)  # Wahrscheinlichkeiten
+    top3_prob, top3_idx = torch.topk(probs, 3)
+
+    # Ergebnisse
+    top3 = [(emotion_labels[i], f"{p.item() * 100:.2f}%") for i, p in zip(top3_idx[0], top3_prob[0])]
+    best_emotion = emotion_labels[top3_idx[0][0]]
+
+    return best_emotion, top3
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=zero_shot_predict,
+    inputs=gr.Image(type="pil"),
+    outputs=["text", gr.Dataframe(headers=["Emotion", "Confidence (%)"])],
+    title="Zero-Shot Emotion Recognition",
+    description="Erkenne Emotionen ohne Training — einfach mit CLIP!"
+)
+
+interface.launch()