Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import text_hammer as th
+from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification
+
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+model = TFDistilBertForSequenceClassification.from_pretrained("Elegbede/Distilbert_FInetuned_For_Text_Classification")
+# Define a function to make predictions
+def predict(texts):
+    # Tokenize and preprocess the new text
+    new_encodings = tokenizer(texts, truncation=True, padding=True, max_length=70, return_tensors='tf')
+    new_predictions = model(new_encodings)
+    # Make predictions
+    new_predictions = model(new_encodings)
+    new_labels_pred = tf.argmax(new_predictions.logits, axis=1)
+    new_labels_pred = new_labels_pred.numpy()[0]
+
+    labels_list = ["Sadness 😭", "Joy 😂", "Love 😍", "Anger 😠", "Fear 😨", "Surprise 😲"]
+    emotion = labels_list[new_labels_pred]
+    return emotion
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs=gr.outputs.Label(num_top_classes = 6),  # Corrected output type
+    examples=[["Tears welled up in her eyes as she gazed at the old family photo."],
+              ["Laughter filled the room as they reminisced about their adventures."], 
+              ["A handwritten note awaited her on the kitchen table, a reminder of his affection."], 
+              ["Harsh words were exchanged in the heated argument."],
+              ["The eerie silence of the abandoned building sent shivers down her spine."],
+              ["She opened the box to find a rare antique hidden inside, a total shock."]
+              ],
+    title="Emotion Classification",
+    description="Predict the emotion associated with a text using my fine-tuned DistilBERT model."
+)
+# Launch the interfac
+iface.launch()