first commit

app.py

@@ -1,47 +1,49 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 
-print("Loading the model...")
-
-# Title and Description
-st.title("Sentiment Analysis Web App")
+st.title("Question Answering Web App")
 st.write("""
 ### Powered by Hugging Face and Streamlit
-This app uses a pre-trained NLP model from Hugging Face to analyze the sentiment of the text you enter.  
-Try entering a sentence to see if it's positive, negative, or neutral!
+This app uses a pre-trained NLP model from Hugging Face to answer questions based on the text you provide.  
+Try entering a context and a question to get an answer!
 """)
 
-# Initialize Hugging Face Sentiment Analysis Pipeline
+# Load the tokenizer and model
 @st.cache_resource
 def load_model():
-    print("before load model")
-    return pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-sentiment_analyzer = load_model()
-
-# Input Text from User
-user_input = st.text_area("Enter some text to analyze:", "Streamlit and Hugging Face make NLP fun!")
-
-# Analyze Sentiment
-if st.button("Analyze Sentiment"):
-    print("button click")
-    if user_input.strip():
-        result = sentiment_analyzer(user_input)[0]
-        sentiment = result['label']
-        score = result['score']
-
-        # Display the Result
-        st.subheader("Sentiment Analysis Result")
-        st.write(f"**Sentiment:** {sentiment}")
-        st.write(f"**Confidence Score:** {score:.2f}")
+    tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
+    model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Define the question-answering pipeline
+@st.cache_resource
+def get_qa_pipeline():
+    return pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+qa_pipeline = get_qa_pipeline()
+
+# UI input for context and question
+context = st.text_area("Enter the context:", "Type the paragraph here where the answer will be extracted.")
+question = st.text_input("Enter the question:", "What is being asked here?")
+
+# Button to perform question answering
+if st.button("Answer Question"):
+    if context and question:
+        result = qa_pipeline(question=question, context=context)
+        answer = result['answer']
+
+        # Display the result
+        st.subheader("Answer")
+        st.write(f"**Answer:** {answer}")
     else:
-        st.warning("Please enter some text to analyze!")
+        st.warning("Please enter both context and question!")
 
 # Sidebar with About Information
 st.sidebar.title("About")
 st.sidebar.info("""
 This app demonstrates the use of Hugging Face's NLP models with Streamlit.  
-It uses the distilbert-base-uncased-finetuned-sst-2-english model for sentiment analysis.
+It uses the `Rakib/roberta-base-on-cuad` model for question answering tasks.
 """)
 
-print('after')