Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import onnxruntime as ort
+from transformers import AutoTokenizer, AutoConfig
+from huggingface_hub import hf_hub_download
+
+# Load model and tokenizer
+repo_id = "iimran/EmotionDetection"
+filename = "model.onnx"
+
+# Download and setup ONNX model
+onnx_model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+config = AutoConfig.from_pretrained(repo_id)
+
+# Get label mapping
+if hasattr(config, "id2label") and config.id2label and len(config.id2label) > 0:
+    id2label = config.id2label
+else:
+    id2label = {
+        0: "anger",
+        1: "fear",
+        2: "joy",
+        3: "love",
+        4: "sadness",
+        5: "surprise",
+        6: "neutral"
+    }
+
+# Create ONNX session
+session = ort.InferenceSession(onnx_model_path)
+
+def predict_emotion(text):
+    """Predict emotion from text"""
+    # Tokenize input
+    inputs = tokenizer(
+        text,
+        return_tensors="np",
+        truncation=True,
+        padding="max_length",
+        max_length=256
+    )
+    
+    # Prepare inputs
+    ort_inputs = {
+        "input_ids": inputs["input_ids"].astype(np.int64),
+        "attention_mask": inputs["attention_mask"].astype(np.int64)
+    }
+    
+    # Run inference
+    outputs = session.run(None, ort_inputs)
+    logits = outputs[0]
+    predicted_class_id = int(np.argmax(logits, axis=-1)[0])
+    
+    # Get label
+    predicted_label = id2label.get(str(predicted_class_id), id2label.get(predicted_class_id, str(predicted_class_id)))
+    
+    # Format output
+    emotion_icons = {
+        "anger": "😠",
+        "fear": "😨",
+        "joy": "😄",
+        "love": "❤️",
+        "sadness": "😢",
+        "surprise": "😲",
+        "neutral": "😐"
+    }
+    
+    icon = emotion_icons.get(predicted_label.lower(), "❓")
+    return f"{icon} {predicted_label}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(label="Enter your text", placeholder="How are you feeling today?"),
+    outputs=gr.Label(label="Predicted Emotion"),
+    title="Emotion Detection",
+    description="Detect emotions in text using iimran/EmotionDetection model",
+    examples=[
+        ["I'm so happy right now!"],
+        ["This situation makes me really angry"],
+        ["I feel anxious about the future"],
+        ["What a beautiful day to be alive!"],
+        ["That news shocked me completely"]
+    ],
+    theme="soft"
+)
+
+# Run the app
+if __name__ == "__main__":
+    demo.launch()