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| 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() |