Create test-ml-code.py

test-ml-code.py

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+import streamlit as st
+import re
+import spacy
+from transformers import pipeline
+
+# Load spaCy's English model
+nlp = spacy.load("en_core_web_sm")
+
+# Basic preprocessing: lowercasing, removing special characters
+def preprocess_text(text):
+    doc = nlp(text.lower())  # Tokenize and lowercase the text
+    tokens = [token.text for token in doc if not token.is_punct]  # Remove punctuation
+    return tokens
+
+# Load pre-trained question-answering model
+qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Function to answer question
+def answer_question(question, context):
+    result = qa_model(question=question, context=context)
+    return result['answer']
+
+# Streamlit App Layout
+st.title("Question Answering App")
+st.write("Upload a text file, ask a question, and get an answer from the text!")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
+
+if uploaded_file is not None:
+    # Read file
+    data = uploaded_file.read().decode('utf-8')
+
+    # Show the content of the file
+    st.write("### File Content")
+    st.write(data)
+
+    # Preprocess the text data
+    processed_data = preprocess_text(data)
+
+    # Ask question
+    question = st.text_input("Enter your question")
+
+    if st.button("Get Answer"):
+        if question:
+            # Get the answer from the QA model
+            answer = answer_question(question, data)
+            st.write(f"**Answer:** {answer}")
+        else:
+            st.write("Please enter a question.")