Update app.py

app.py

@@ -1,84 +1,84 @@
-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()
+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.change(
+        fn=emojiprediction.predict_emoji,
+        inputs=textbox,
+        outputs=[textbox1, textbox2]
+    )
+
+
+if __name__ == "__main__":
+    app.launch()