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app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+import joblib
+from transformers import AutoTokenizer
+import numpy as np
+from dotenv import load_dotenv
+import os
+import onnxruntime as ort
+
+
+class EmojiPrediction:
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.label_map = None
+        self.thresholds = None
+        self.repo_id = "ashish-001/tweet-emoji-predictor"
+        self.load_model()
+        self.load_required_data()
+
+    def load_model(self):
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.repo_id)
+        onnx_file = hf_hub_download(
+            repo_id=self.repo_id,
+            filename='model_quantized.onnx'
+        )
+
+        self.model = ort.InferenceSession(onnx_file)
+
+    def load_required_data(self):
+        filepath = hf_hub_download(
+            repo_id=self.repo_id,
+            filename='mlb_emoji_encoder.pkl'
+        )
+        with open(filepath, 'rb') as f:
+            self.label_map = joblib.load(f)
+
+        threshold_filepath = hf_hub_download(
+            repo_id=self.repo_id,
+            filename='thresholds.npy'
+        )
+        self.thresholds = np.load(threshold_filepath)
+
+    def predict_emoji(self, text):
+        if not len(text.strip()):
+            return "", ""
+        inputs = self.tokenizer(
+            text,
+            return_tensors="np"
+        )
+
+        # with torch.no_grad():
+        onnx_inputs = {
+            "input_ids": inputs["input_ids"].astype(np.int64),
+            "attention_mask": inputs["attention_mask"].astype(np.int64),
+        }
+        logits = self.model.run(None, onnx_inputs)[0]
+        probs = np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)
+        predicted_labels = (probs >= self.thresholds).astype(
+            int).reshape(1, -1)
+
+        emojis = "".join(self.label_map.inverse_transform(predicted_labels)[0])
+        return emojis, f"{text} {emojis}"
+
+
+emojiprediction = EmojiPrediction()
+
+with gr.Blocks() as app:
+    gr.Markdown("# Tweet/Text Emoji Predictor")
+    with gr.Row(equal_height=True):
+        textbox = gr.Textbox(lines=1, label="User Input",
+                             placeholder="Start entering the text")
+        textbox1 = gr.Textbox(label="Raw Emoji Output")
+        textbox2 = gr.Textbox(label="Text with Emojis")
+    textbox.input(
+        fn=emojiprediction.predict_emoji,
+        inputs=textbox,
+        outputs=[textbox1, textbox2]
+    )
+
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+scikit-learn==1.7.0
+emoji==2.14.1
+transformers==4.52.4
+onnxruntime==1.17.1
+numpy==1.26.4
+huggingface-hub==0.33.0
+gradio==5.34.0
+joblib==1.5.1