Update app.py

app.py

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+# app.py
+import streamlit as st
+import pandas as pd
+import re
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the model and tokenizer
+@st.cache(allow_output_mutation=True)
+def load_model():
+    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=8)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Custom sentiments
+sentiments = ["happy", "motivated", "growth", "optimistic", "jealousy", "frustrated", "decline", "angry"]
+
+# Define the preprocessing function
+def preprocess_text(text):
+    text = re.sub(r'[^\w\s]', '', text.lower())  # Remove punctuation and lowercase
+    text = re.sub(r'\d+', '', text)  # Remove numbers
+    return text
+
+# Title and instructions
+st.title("Sentiment Analysis of Financial News")
+st.write("Enter a sentence to analyze its sentiment across predefined categories.")
+
+# Input text from user
+text = st.text_input("Enter a sentence:", "")
+
+if text:
+    # Preprocess and tokenize
+    cleaned_text = preprocess_text(text)
+    inputs = tokenizer(cleaned_text, return_tensors="pt", truncation=True, padding=True)
+    
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        sentiment_score = outputs.logits.softmax(dim=1)
+
+    # Convert tensor to list for plotting
+    score_list = sentiment_score.tolist()[0]
+
+    # Display sentiment scores as a table
+    st.subheader("Sentiment Scores")
+    score_df = pd.DataFrame({"Sentiment": sentiments, "Score": score_list})
+    st.dataframe(score_df)
+
+    # Plot the sentiment scores
+    st.subheader("Sentiment Score Chart")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    mustard_yellow = "#FFDB58"
+
+    # Plot bars with spacing and color
+    ax.bar(np.arange(len(sentiments)) * 1.5, score_list, color=mustard_yellow, edgecolor="black", width=0.8)
+    
+    # Customize the plot
+    ax.set_xlabel("Sentiments", color="black", fontsize=12)
+    ax.set_ylabel("Scores", color="black", fontsize=12)
+    ax.set_title("Sentiment Analysis of Financial News", color="black", fontsize=14)
+    ax.set_xticks(np.arange(len(sentiments)) * 1.5)
+    ax.set_xticklabels(sentiments, color="black", fontsize=10, rotation=45)
+    ax.tick_params(axis="y", colors="black")
+
+    # Display the plot in Streamlit
+    st.pyplot(fig)