Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import re
+import os
+import gdown
+from sentence_transformers import SentenceTransformer
+
+# Download the file
+file_id = '1P3Nz6f3KG0m0kO_2pEfnVIhgP8Bvkl4v'
+url = f'https://drive.google.com/uc?id={file_id}'
+excel_file_path = os.path.join(os.path.expanduser("~"), 'medical_data.csv')
+
+gdown.download(url, excel_file_path, quiet=False)
+
+# Read the CSV file into a DataFrame using 'latin1' encoding
+try:
+    medical_df = pd.read_csv(excel_file_path, encoding='utf-8')
+except UnicodeDecodeError:
+    medical_df = pd.read_csv(excel_file_path, encoding='latin1')
+
+def remove_digits_with_dot(input_string):
+    # Define a regex pattern to match digits with a dot at the beginning of the string
+    pattern = re.compile(r'^\d+\.')
+
+    # Use sub() method to replace the matched pattern with an empty string
+    result_string = re.sub(pattern, '', input_string)
+
+    return result_string
+
+medical_df["Questions"] = medical_df["Questions"].apply(remove_digits_with_dot)
+
+medical_df = medical_df[medical_df["Answers"].notna()]
+
+from InstructorEmbedding import INSTRUCTOR
+
+model = INSTRUCTOR("hkunlp/instructor-large")
+corpus = medical_df["Answers"].apply(lambda x:[x]).tolist()
+answer_embeddings = []
+for answer in corpus:
+    answer_embeddings.append(model.encode(answer))
+    
+answer_embeddings = np.array(answer_embeddings)
+answer_embeddings = answer_embeddings.reshape(148, 768)
+
+def get_answer(query):
+    
+    query = [['Represent the Wikipedia question for retrieving supporting documents: ', query]]
+
+    query_embedding = model.encode(query)
+
+    similarities = cosine_similarity(query_embedding, answer_embeddings)
+
+    retrieved_doc_id = np.argmax(similarities)
+
+    q = medical_df.iloc[retrieved_doc_id]["Questions"]
+    a = medical_df.iloc[retrieved_doc_id]["Answers"]
+    r = medical_df.iloc[retrieved_doc_id]["References"]
+    
+    return (q, a, r)
+
+# Streamlit app
+st.title("Medical QA System")
+
+user_input = st.text_input("Ask a medical question:")
+if user_input:
+    result = get_answer(user_input)
+    st.subheader("Question:")
+    st.write(result[0])
+    st.subheader("Answer:")
+    st.write(result[1])
+    st.subheader("References:")
+    st.write(result[2])