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import torch
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
import matplotlib.pyplot as plt
import streamlit as st
# Load pre-trained model and tokenizer
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
# Function to classify text
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
scores = torch.nn.functional.softmax(outputs.logits, dim=1)
return scores
# Streamlit interface
st.title("NLP Transformer with PyTorch and Hugging Face")
prompt_text = "Crt n NLP trnsfrmr xmpl sng PyTrch wth Hggng Fc, dd Strmlnt ntrfc fr npts nd tpts, ncld mtlpl grph f ncssry. Cd shld b sy t ct nd pst"
st.write(f"**Prompt:** {prompt_text}")
st.header("Sentiment Analysis")
text = st.text_area("Enter text for sentiment analysis:")
if st.button("Classify"):
scores = classify_text(text).detach().numpy()[0]
labels = ["1 star", "2 stars", "3 stars", "4 stars", "5 stars"]
st.write("Classification Scores:")
for label, score in zip(labels, scores):
st.write(f"{label}: {score:.4f}")
fig, ax = plt.subplots()
ax.bar(labels, scores, color='blue')
ax.set_xlabel('Sentiment')
ax.set_ylabel('Score')
ax.set_title('Sentiment Analysis Scores')
st.pyplot(fig)
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