Upload app.py

app.py

@@ -0,0 +1,67 @@
+import os
+import streamlit as st
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS 
+from transformers import TFAutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+os.environ["OPENAI_API_KEY"] = "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"
+
+# Read data
+with open("./data/full_context.txt", "r") as file1:
+    doc = file1.read()
+
+# Splitting up the text into smaller chunks for indexing
+text_splitter = CharacterTextSplitter(        
+    separator = "\n",
+    chunk_size = 1000,
+    chunk_overlap  = 200, #striding over the text
+    length_function = len,
+)
+texts = text_splitter.split_text(doc)
+
+
+# Download embeddings from OpenAI
+embeddings = OpenAIEmbeddings()
+docsearch = FAISS.from_texts(texts, embeddings)
+
+# Load model
+model_path = "/content/drive/MyDrive/Colab_Notebooks/COS30081_NLP/D_HD_Task/models/roberta_model"
+
+model = TFAutoModelForQuestionAnswering.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained('deepset/roberta-base-squad2')
+
+# Initialize Transformer pipeline with our own model and tokenizer
+question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+def findHighestScore(question):
+    docs_found = docsearch.similarity_search(question)
+    doc_score = 0.5
+    doc_answer = ''
+
+    for doc in docs_found:
+        doc_result = question_answerer(question=question, context = doc.page_content)
+        if doc_result['score'] > doc_score:
+            doc_score = doc_result['score']
+            doc_answer = doc_result['answer']
+    
+    return doc_answer, doc_score
+
+
+def QnAfunction(question):
+    answer1, score1 = findHighestScore(question)
+    if answer1 != '':
+        return answer1, score1
+        # print("Answer: ", answer1)
+        # print("Score: ", score1)
+
+    else:
+        return "No Answer found. Please ask question related to Bachelor of Computer Science program at Swinburne.", 0
+        # print("No Answer found. Please ask question related to Bachelor of Computer Science program at Swinburne.")
+
+
+text = st.text_area("Ask any question about the Bachelor of Computer Science program at Swinburne: ")
+if text:
+    ans, score = QnAfunction(text)
+    st.json(ans)
+