Added all UI files

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/sentiment_analysis.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,34 @@
+from pyexpat import model
+from fastai.text.all import *
+import gradio as gr
+
+learner = load_learner('models/sentiment_analysis.pkl')
+
+sent_dict = {
+    'pos': 'Positive', 
+    'neg': 'Negative',
+    'neutral': 'Neutral'
+}
+
+def predict_sentiment(input_text):
+    map_dict = learner.dls.categorize.vocab.o2i
+    predicted_sentiment = learner.predict(input_text)[0]
+    predicted_confidence = float(list(learner.predict(input_text)[2])[map_dict[learner.predict(input_text)[0]]])
+    flag = False
+    threshold = 0.5
+    if predicted_confidence < threshold:
+        predicted_sentiment = 'neutral'
+        flag = True
+
+    # return{
+    #     'sentiment': sent_dict[predicted_sentiment],
+    #     'confidence': predicted_confidence,
+    #     'below_threshold': flag,
+    # }
+    return str(sent_dict[predicted_sentiment])
+
+inputs = gr.Textbox()
+outputs = "text"
+title = "Sentiment Analysis"
+description = "Gradio demo for Sentiment Analysis. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below."
+gr.Interface(predict_sentiment, inputs, outputs, title=title, description=description).launch()

models/sentiment_analysis.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afbd42cd88301e868a99dea9cac833209c8d0fcc83fe3c9fae8497fc65b93d7e
+size 231439231

requirements.txt

@@ -0,0 +1 @@
+fastai