build v1.2

app.py

@@ -1,4 +1,36 @@
 import streamlit as st
+import altair as alt
+from transformers import pipeline
+from transformers import AutoModelForSeq2SeqLM , AutoTokenizer, TranslationPipeline
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+
+
+st.title('NLP Question Answering 🕵️‍♂️')
+st.write('Ask a question about NLP, and I will answer based on the provided context! 🔄')
+user_input = st.text_area("enter question about NLP", "What is tokenizer?")
+@st.cache_resource
+def load_model():
+    print("Loading model...")
+    return pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+
+dunno_answerer  = load_model()
+with open('NLP_History_and_Facts.txt', 'r') as file:
+    context = file.read()
+
+if st.button("Answer!"):
+    if user_input.strip():
+        # Generate an answer using the model
+        result = dunno_answerer(question=user_input, context=context)
+
+        # Display the answer and additional information
+        st.write(f"**Answer:** {result['answer']}")
+        st.write(f"**Confidence Score:** {round(result['score'], 4)}")
+        st.write(f"**Answer Start Position:** {result['start']}")
+        st.write(f"**Answer End Position:** {result['end']}")
+    else:
+        st.write("Please enter a valid question!")
+
+# x = st.slider('Select a value')
+# st.write(x, 'squared is', x * x)
+# print(x)