Update README.md

README.md

@@ -80,25 +80,41 @@ The following hyperparameters were used during training:
 - Datasets 3.1.0
 - Tokenizers 0.20.3
 
-### code to use in pipeline
-'''python 
-import matplotlib.pyplot as plt
+## Finance Sentiment Predictor
+
+
+```python
 import plotly.graph_objects as go
 %matplotlib inline
 from transformers import pipeline
-classifier = pipeline("text-classification", model="Sharpaxis/FIN_BERT_sentiment",top_k=None)
+
+# Load the sentiment analysis pipeline
+classifier = pipeline("text-classification", model="Sharpaxis/FIN_BERT_sentiment", top_k=None)
+
 def finance_sentiment_predictor(text):
     text = str(text)
     out = classifier(text)[0]
     scores = [sample['score'] for sample in out]
-    labels = [sample['label'] for sample in out ]
-    label_map = {'LABEL_0':"Negative",'LABEL_1':"Neutral",'LABEL_2':"Positive"}
-    sentiments = [label_map[label] for label  in labels]
+    labels = [sample['label'] for sample in out]
+    label_map = {'LABEL_0': "Negative", 'LABEL_1': "Neutral", 'LABEL_2': "Positive"}
+    sentiments = [label_map[label] for label in labels]
+    
     for i in range(len(scores)):
         print(f"{sentiments[i]} : {scores[i]}")
+    
     print(f"Sentiment of text is {sentiments[np.argmax(scores)]}")
+    
+    # Create the bar chart
     fig = go.Figure(
-        data=[go.Bar(x=sentiments,y=scores,marker=dict(color=["red", "blue", "green"]),width=0.3)])
+        data=[
+            go.Bar(
+                x=sentiments,
+                y=scores,
+                marker=dict(color=["red", "blue", "green"]),
+                width=0.3  # Adjust bar width
+            )
+        ]
+    )
     fig.update_layout(
         title="Sentiment Analysis Scores",
         xaxis_title="Sentiments",