Create app.py

app.py

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+pip install transformers[sentencepiece]
+import streamlit as st
+from transformers import pipeline
+
+
+# Load the summarization & translation model pipeline
+tran_sum_pipe = pipeline("translation", model='utrobinmv/t5_summary_en_ru_zh_base_2048',return_all_scores=True)
+sentiment_pipeline = pipeline("text-classification", model='Howosn/Sentiment_Model',return_all_scores=True)
+#tokenizer = AutoTokenizer.from_pretrained('Howosn/Sentiment_Model', use_fast=False)
+
+# Streamlit application title
+st.title("Emotion analysis")
+st.write("Turn Your Input Into Sentiment Score")
+
+# Text input for the user to enter the text to analyze
+text = st.text_area("Enter the text", "")
+
+# Perform analysis result when the user clicks the "Analyse" button
+if st.button("Analyse"):
+    # Perform text classification on the input text
+    trans_sum = tran_sum_pipe(text)[0]
+    results = sentiment_pipeline(trans_sum)[0]
+
+    # Display the classification result
+    max_score = float('-inf')
+    max_label = ''
+
+    for result in results:
+        if result['score'] > max_score:
+            max_score = result['score']
+            max_label = result['label']
+
+    st.write("Text:", text)
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)