Parse SA pred str

app.py

@@ -1,3 +1,4 @@
+import json
 import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModel, BertForSequenceClassification, AlbertForSequenceClassification, DebertaForSequenceClassification, AutoModelForSequenceClassification, RobertaForSequenceClassification
 from peft.auto import AutoPeftModelForSequenceClassification
@@ -7,7 +8,20 @@ from huggingface_hub import hf_hub_download
 import plotly.express as px
 import pandas as pd
 
+# Parse sentiment analysis pipeline results
+def parse_pipe_sa(pipe_out_text: str):
+    output_list = list(pipe_out_text)
+    pipe_label = output_list[0]['label']
+    pipe_score = output_list[0]['score']
 
+    parsed_prediction = 'NULL'
+
+    if pipe_label == 'NEGATIVE' or pipe_label == 'LABEL_0':
+        parsed_prediction = f'This model thinks the sentiment is negative with a confidence score of {pipe_score}'
+    elif pipe_label == 'POSITIVE' or pipe_label == 'LABEL_1':
+        parsed_prediction = f'This model thinks the sentiment is positive with a confidence score of {pipe_score}'
+
+    return parsed_prediction
 
 loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA")
 #tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
@@ -28,13 +42,13 @@ SentimentAnalysis_LORA_pipe = pipeline("sentiment-analysis", model=sa_merged_mod
 
 #text class models 
 def distilBERTnoLORA_fn(text):
-    return distilBERTnoLORA_pipe(text)
+    return parse_pipe_sa(distilBERTnoLORA_pipe(text))
 
 def distilBERTwithLORA_fn(text):
-    return SentimentAnalysis_LORA_pipe(text)
+    return parse_pipe_sa(SentimentAnalysis_LORA_pipe(text))
 
 def distilBERTUntrained_fn(text):
-    return distilBERTUntrained_pipe(text)
+    return parse_pipe_sa(distilBERTUntrained_pipe(text))
 
 
 # Handle calls to ALBERT---------------------------------------------
@@ -335,7 +349,7 @@ with gr.Blocks(
                     TextClassUntrained = gr.Textbox(label = "Training Informaiton")
 
                 with gr.Row(variant="panel"):
-                    TextClassOut1 = gr.Textbox(label= "Conventionaly Trained Model")
+                    TextClassOut1 = gr.Textbox(label="Conventionaly Trained Model")
                     TextClassNoLoraStats = gr.Textbox(label = "Training Informaiton - Active Training Time: 27.95 mins")
 
                 with gr.Row(variant="panel"):