Update README.md

README.md

@@ -84,29 +84,58 @@ The following hyperparameters were used during training:
 
 
 ```python
+import matplotlib.pyplot as plt
 import plotly.graph_objects as go
-%matplotlib inline
+from IPython.display import display, HTML
+import numpy as np
 from transformers import pipeline
+%matplotlib inline
 
-# Load the sentiment analysis pipeline
-classifier = pipeline("text-classification", model="Sharpaxis/FIN_BERT_sentiment", top_k=None)
+# Pipelines
+classifier = pipeline("text-classification", model="Sharpaxis/Finance_DistilBERT_sentiment", top_k=None)
+pipe = pipeline("text-classification", model="Sharpaxis/News_classification_distilbert")
 
-def finance_sentiment_predictor(text):
+def finance_text_predictor(text):
     text = str(text)
     out = classifier(text)[0]
+    type_news = pipe(text)[0]
+    
+    # Display news type and text in HTML
+    if type_news['label'] == 'LABEL_1':
+        display(HTML(f"""
+        <div style="border: 2px solid red; padding: 10px; margin: 10px; background-color: #ffe6e6; color: black; font-weight: bold;">
+            IMPORTANT TECH/FIN News<br>
+            <div style="margin-top: 10px; font-weight: normal; font-size: 14px; color: darkred;">{text}</div>
+        </div>
+        """))
+    elif type_news['label'] == 'LABEL_0':
+        display(HTML(f"""
+        <div style="border: 2px solid green; padding: 10px; margin: 10px; background-color: #e6ffe6; color: black; font-weight: bold;">
+            NON IMPORTANT NEWS<br>
+            <div style="margin-top: 10px; font-weight: normal; font-size: 14px; color: darkgreen;">{text}</div>
+        </div>
+        """))
+    
+    # Sentiment analysis scores
     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]
+    
+    print("SCORES")
     for i in range(len(scores)):
-        print(f"{sentiments[i]} : {scores[i]}")
+        print(f"{sentiments[i]} : {scores[i]:.4f}")
+    
     print(f"Sentiment of text is {sentiments[np.argmax(scores)]}")
+    
+    # Bar chart for sentiment scores
     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)]
+    )
     fig.update_layout(
         title="Sentiment Analysis Scores",
         xaxis_title="Sentiments",
         yaxis_title="Scores",
         template="plotly_dark"
     )
-    fig.show()
+    fig.show()