Spaces:
Sleeping
Sleeping
File size: 4,677 Bytes
2da2f3b |
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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
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 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)
# Calculate word matches and LCS bonus
file_matches = {}
for file, tokens in processed_files.items():
single_matches = set(tokens) & set(processed_prompt)
double_matches = set(nltk.bigrams(tokens)) & set(nltk.bigrams(processed_prompt))
triple_matches = set(nltk.trigrams(tokens)) & set(nltk.trigrams(processed_prompt))
match_count = len(single_matches) + len(double_matches) * 4 + len(triple_matches) * 9
file_matches[file] = {
'single_matches': single_matches,
'double_matches': double_matches,
'triple_matches': triple_matches,
'match_count': match_count
}
# Sort the files by the match count
sorted_files = sorted(file_matches.items(), key=lambda x: x[1]['match_count'], reverse=True)
# Create a markdown outline with match counts and word matches
outline = "## Outline\n"
for file, matches in sorted_files:
outline += f"- {file}: {matches['match_count']} matches\n"
if matches['single_matches']:
outline += f" - Single word matches: {', '.join(matches['single_matches'])}\n"
if matches['double_matches']:
outline += f" - Double word matches: {', '.join(' '.join(pair) for pair in matches['double_matches'])}\n"
if matches['triple_matches']:
outline += f" - Triple word matches: {', '.join(' '.join(trio) for trio in matches['triple_matches'])}\n"
# Create a single prompt by concatenating the original prompt and the content of the top ten files
context_prompt = prompt
for file, _ in sorted_files[:10]:
with open(file, 'r') as f:
content = f.read()
# Highlight the matching words in bold
for word in file_matches[file]['single_matches']:
content = content.replace(word, f"**{word}**")
for pair in file_matches[file]['double_matches']:
content = content.replace(' '.join(pair), f"**{' '.join(pair)}**")
for trio in file_matches[file]['triple_matches']:
content = content.replace(' '.join(trio), f"**{' '.join(trio)}**")
context_prompt += '\n\n' + content
# Create a plotly graph showing the match counts for the top ten files
fig = px.bar(x=[file for file, _ in sorted_files[:10]], y=[matches['match_count'] for _, matches in sorted_files[:10]])
fig.update_layout(xaxis_title='File', yaxis_title='Match Count')
st.plotly_chart(fig)
return outline, 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"):
outline, context_prompt = get_context_files(prompt, md_files)
st.subheader("Outline")
st.markdown(outline)
st.subheader("Context Prompt")
st.markdown(context_prompt)
if __name__ == '__main__':
main() |