First Version

app.py

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+import onnxruntime as ort
+from transformers import AutoTokenizer
+import gradio as gr
+
+# Define available models with their ONNX file paths and tokenizer names
+models = {
+    "DistilBERT": {
+        "onnx_model_path": "distilbert.onnx",
+        "tokenizer_name": "distilbert-base-multilingual-cased",
+    },
+    "BERT": {
+        "onnx_model_path": "bert.onnx",
+        "tokenizer_name": "bert-base-multilingual-cased",
+    },
+    "MuRIL": {
+        "onnx_model_path": "muril.onnx",
+        "tokenizer_name": "google/muril-base-cased",
+    },
+    "RoBERTa": {
+        "onnx_model_path": "roberta.onnx",
+        "tokenizer_name": "cardiffnlp/twitter-roberta-base-emotion",
+    },
+}
+
+# Load models and tokenizers into memory
+model_sessions = {}
+tokenizers = {}
+
+for model_name, config in models.items():
+    print(f"Loading {model_name}...")
+    model_sessions[model_name] = ort.InferenceSession(config["onnx_model_path"])
+    tokenizers[model_name] = AutoTokenizer.from_pretrained(config["tokenizer_name"])
+
+print("All models loaded!")
+
+# Prediction function
+def predict_with_model(text, model_name):
+    # Select the appropriate ONNX session and tokenizer
+    ort_session = model_sessions[model_name]
+    tokenizer = tokenizers[model_name]
+
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True)
+
+    # Run ONNX inference
+    outputs = ort_session.run(None, {
+        "input_ids": inputs["input_ids"],
+        "attention_mask": inputs["attention_mask"],
+    })
+
+    # Post-process the output
+    logits = outputs[0]
+    label = "Hate Speech" if logits[0][1] > logits[0][0] else "Not Hate Speech"
+    return label
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=predict_with_model,
+    inputs=[
+        gr.Textbox(label="Enter text to classify"),
+        gr.Dropdown(
+            choices=list(models.keys()),
+            label="Select a model",
+        ),
+    ],
+    outputs="text",
+    title="Multi-Model Hate Speech Detection",
+    description="Choose a model and enter text to classify whether it's hate speech.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

bert.onnx

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+oid sha256:4d38eb2aeab1422656bed7ceb8a0979ea43cd65f5d3f80cdc3d73f1f02482cb1
+size 711692681

distilbert.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cfada1fc91fbf304085d4e72d3fc8c47ad3059196adca64f9dad4341c8b8f82
+size 541440517

muril.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1142234505ed706b0d9f9a9eb5b2d5b9079647b52ea2b918bb96884758c8395f
+size 950503817

roberta.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5aa28d6d11a3e37527d7f9428069afb92464cb0c89384ba6e1b31d497466f21
+size 498870601