Update app.py

app.py

@@ -1,31 +1,48 @@
-import streamlit as st
-from transformers import pipeline
-
-# Title of the app
-st.title("Hugging Face Transformers Demo")
-st.write("Enter text to analyze sentiment or generate new text:")
-
-# Load pre-trained models
-sentiment_analyzer = pipeline("sentiment-analysis")
-text_generator = pipeline("text-generation", model="gpt2")
-
-# User input
-user_input = st.text_area("Input Text")
-
-# Sentiment Analysis
-if st.button("Analyze Sentiment"):
-    if user_input:
-        result = sentiment_analyzer(user_input)
-        st.write("Sentiment Analysis Result:")
-        st.write(result)
-    else:
-        st.write("Please enter some text to analyze.")
-
-# Text Generation
-if st.button("Generate Text"):
-    if user_input:
-        result = text_generator(user_input, max_length=50)
-        st.write("Generated Text:")
-        st.write(result[0]['generated_text'])
-    else:
-        st.write("Please enter some text to generate.")
+import streamlit as st
+from transformers import pipeline
+
+# Title of the app
+st.title("Multi-Task NLP App with Hugging Face Transformers")
+st.write("Explore advanced NLP tasks: Sentiment Analysis, Text Summarization, and Question Answering.")
+
+# Load pre-trained models
+sentiment_analyzer = pipeline("sentiment-analysis")
+summarizer = pipeline("summarization")
+qa_pipeline = pipeline("question-answering")
+
+# Sidebar for task selection
+task = st.sidebar.selectbox("Choose a Task", ["Sentiment Analysis", "Text Summarization", "Question Answering"])
+
+if task == "Sentiment Analysis":
+    st.header("Sentiment Analysis")
+    user_input = st.text_area("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        if user_input:
+            result = sentiment_analyzer(user_input)
+            st.write("Sentiment Analysis Result:")
+            st.write(result)
+        else:
+            st.write("Please enter some text to analyze.")
+
+elif task == "Text Summarization":
+    st.header("Text Summarization")
+    user_input = st.text_area("Enter text to summarize:")
+    if st.button("Summarize"):
+        if user_input:
+            result = summarizer(user_input, max_length=50, min_length=25, do_sample=False)
+            st.write("Summary:")
+            st.write(result[0]['summary_text'])
+        else:
+            st.write("Please enter some text to summarize.")
+
+elif task == "Question Answering":
+    st.header("Question Answering")
+    context = st.text_area("Enter context (the text to ask questions about):")
+    question = st.text_input("Enter your question:")
+    if st.button("Get Answer"):
+        if context and question:
+            result = qa_pipeline(question=question, context=context)
+            st.write("Answer:")
+            st.write(result['answer'])
+        else:
+            st.write("Please provide both context and question.")