Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import matplotlib.pyplot as plt
+
+# Initialize tokenizer and model once to avoid reloading them on every interaction
+@st.cache(allow_output_mutation=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+    model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+st.title("Sentiment Analysis App")
+
+text = st.text_input("Enter text to analyze:")
+if st.button("Analyze"):
+    encoding = tokenizer.encode_plus(text, return_tensors="pt", padding=True, truncation=True)
+    input_ids = encoding["input_ids"]
+    attention_mask = encoding["attention_mask"]
+
+    with torch.no_grad():
+        output = model(input_ids, attention_mask)
+    prediction = int(torch.argmax(output.logits))
+
+    # Detailed sentiment output
+    sentiment = ["Negative", "Neutral", "Positive"][prediction]
+    st.write(f"Sentiment: {sentiment}")
+
+    values = output.logits.squeeze().tolist()  # Flatten the logits tensor to a list
+    labels = ["Negative", "Neutral", "Positive"]
+    
+    # Plotting
+    fig, ax = plt.subplots()
+    ax.bar(labels, values, color=['red', 'blue', 'green'])
+    ax.set_title("Sentiment Analysis Scores")
+    ax.set_ylabel("Score")
+    st.pyplot(fig)