Update main.py

main.py

@@ -1,30 +1,41 @@
 import streamlit as st
-import transformers
 from transformers import pipeline
 
+# Load pipelines directly (no functions)
 text_classification = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
 ques_ans = pipeline("question-answering", model="deepset/roberta-base-squad2")
 summarization = pipeline("summarization", model="facebook/bart-large-cnn")
 
-st.title("NLP Mini Toolkit")
+# Title
+st.title("🔧 Simple NLP Toolkit")
 
+# Select Task
 task = st.radio("Select NLP Task", ("Sentiment Analysis", "Question Answering", "Summarization"))
 
+# Sentiment Analysis
 if task == "Sentiment Analysis":
     st.subheader("Sentiment Analysis")
     user_text = st.text_input("Enter text:")
     if user_text:
-        st.write(text_classification(user_text))
+        result = text_classification(user_text)[0]
+        st.write(f"**Sentiment:** {result['label']}")
+        st.write(f"**Confidence:** {result['score'] * 100:.2f}%")
 
+# Question Answering
 elif task == "Question Answering":
     st.subheader("Question Answering")
-    question = st.text_input("Question:")
-    context = st.text_area("Context:")
+    question = st.text_input("Enter your question:")
+    context = st.text_area("Enter context:")
     if question and context:
-        st.write(ques_ans(question=question, context=context))
+        result = ques_ans(question=question, context=context)
+        st.write(f"**Answer:** {result['answer']}")
+        st.write(f"**Confidence:** {result['score'] * 100:.2f}%")
 
+# Summarization
 elif task == "Summarization":
     st.subheader("Summarization")
     text_to_summarize = st.text_area("Enter text to summarize:")
     if text_to_summarize:
-        st.write(summarization(text_to_summarize))
+        result = summarization(text_to_summarize, max_length=130, min_length=30, do_sample=False)
+        st.write("**Summary:**")
+        st.write(result[0]['summary_text'])