Spaces:
Sleeping
Sleeping
File size: 3,243 Bytes
491710e b9db024 491710e b9db024 491710e b9db024 491710e b9db024 491710e b9db024 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import os
import zipfile
import streamlit as st
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import plotly.express as px
nltk.download('punkt')
nltk.download('stopwords')
def preprocess_text(text):
# Tokenize the text and remove stopwords
tokens = nltk.word_tokenize(text.lower())
stop_words = set(stopwords.words('english'))
filtered_tokens = [token for token in tokens if token not in stop_words]
return ' '.join(filtered_tokens)
def get_context_files(prompt, md_files):
# Preprocess the prompt and context files
processed_prompt = preprocess_text(prompt)
processed_files = {}
for file in md_files:
with open(file, 'r') as f:
content = f.read()
processed_files[file] = preprocess_text(content)
# Create a CountVectorizer to calculate word counts
vectorizer = CountVectorizer()
file_vectors = vectorizer.fit_transform(processed_files.values())
prompt_vector = vectorizer.transform([processed_prompt])
# Calculate the number of matching words for each file
match_counts = prompt_vector.dot(file_vectors.T).toarray()[0]
# Sort the files by the number of matching words
sorted_files = sorted(zip(md_files, match_counts), key=lambda x: x[1], reverse=True)
# Get the top ten files
top_ten_files = [file for file, count in sorted_files[:10]]
# Create a single prompt by concatenating the original prompt and the content of the top ten files
context_prompt = prompt
for file in top_ten_files:
with open(file, 'r') as f:
context_prompt += '\n\n' + f.read()
# Create a plotly graph showing the counts of matching words for the top ten files
fig = px.bar(x=[file for file, count in sorted_files[:10]], y=[count for file, count in sorted_files[:10]])
fig.update_layout(xaxis_title='File', yaxis_title='Number of Matching Words')
st.plotly_chart(fig)
return context_prompt
# Streamlit app
def main():
st.title("Context-Aware Prompt Evaluation")
# File upload
uploaded_file = st.file_uploader("Upload a zip file with .md files", type="zip")
if uploaded_file is not None:
# Unzip the uploaded file
with zipfile.ZipFile(uploaded_file, 'r') as zip_ref:
zip_ref.extractall('uploaded_files')
# Get the list of .md files from the uploaded directory
md_files = [os.path.join('uploaded_files', file) for file in os.listdir('uploaded_files') if file.endswith('.md')]
# Show the list of files
st.subheader("Uploaded Files")
for file in md_files:
st.write(file)
# Prompt input
prompt = st.session_state.get('prompt', 'What are the main use cases of generative AI in healthcare that are currently unsolved?')
prompt = st.text_area("Enter your prompt", value=prompt, key='prompt')
# Evaluate the files for the prompt
if st.button("Evaluate"):
context_prompt = get_context_files(prompt, md_files)
st.subheader("Context Prompt")
st.write(context_prompt)
if __name__ == '__main__':
main() |