initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+BERT_sentiment_analysis.pth filter=lfs diff=lfs merge=lfs -text

BERT_sentiment_analysis.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7336efc4b2f17e373689bf5cef49d7a9c8eee0288c98a7179be8fdacc0297316
+size 267884656

app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from transformers import DistilBertTokenizerFast
+from timeit import default_timer as timer
+
+# Setup class names
+class_names = ["Positive", "Negative",]
+
+### 2. Load the model ###
+
+model = torch.load(f="bert_sentiment_classifier/BERT_sentiment_analysis.pth",
+                   map_location=torch.device("cpu"))  # load to CPU)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(text: str):
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+
+    tokenizer = DistilBertTokenizerFast.from_pretrained(
+      'distilbert-base-uncased'
+    )
+
+    input = tokenizer(text, return_tensors="pt").to(DEVICE)
+
+    model.eval()
+    with torch.inference_mode():
+
+      logits = model(**input).logits
+      predicted_class_id = logits.argmax().item()
+
+    if predicted_class_id == 1:
+      result = "Positive 😊"
+    else:
+      result = "Negative 🙁"
+
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+
+    # Return the prediction dictionary and prediction time
+    return result
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "Sentiment Classifier"
+description = "A Sentiment Classifier trained by fine-tuning [DistilBert](https://huggingface.co/docs/transformers/v4.42.0/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification) Transformer model using hugging face [transformers](https://huggingface.co/docs/transformers/en/index) library."
+article = "The model classifies sentiment of an input text (whether the text shows a positive or negative sentiment)."
+
+#Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=[gr.Textbox(label="Input")], 
+                    outputs=[gr.Label(label="Prediction")],
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

requirements.txt

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+torch
+gradio
+pandas
+transformers
+torchtext