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import difflib |
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import json |
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import numpy as np |
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import streamlit as st |
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from pyserini.search.lucene import LuceneSearcher |
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def read_json(file_name): |
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with open(file_name, "r") as f: |
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json_data = json.load(f) |
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return json_data |
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class SearchApplication: |
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def __init__(self): |
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self.title = "Awesome ChatGPT repositories search" |
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self.set_page_config() |
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self.searcher = self.set_searcher() |
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st.header(self.title) |
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col1, col2 = st.columns(2) |
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with col1: |
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self.query = st.text_input("Search English words", value="") |
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with col2: |
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st.write("#") |
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self.search_button = st.button("🔎") |
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st.caption( |
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"You can search for open-source software from [1250+ " |
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" repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories)." |
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) |
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st.write("#") |
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candidate_words_file = "candidate_words.json" |
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candidate_words_json = read_json(candidate_words_file) |
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self.candidate_words = candidate_words_json["candidate_words"] |
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self.show_popular_words() |
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self.show_search_results() |
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def set_page_config(self): |
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st.set_page_config( |
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page_title=self.title, |
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page_icon="😎", |
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layout="centered", |
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) |
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def set_searcher(self): |
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searcher = LuceneSearcher("indexes/docs") |
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return searcher |
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def show_popular_words(self): |
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st.caption("Popular words") |
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word1, word2, word3, word4, word5, word6 = st.columns(6) |
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with word1: |
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button1 = st.button("Prompt") |
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if button1: |
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self.query = "prompt" |
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with word2: |
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button2 = st.button("Chatbot") |
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if button2: |
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self.query = "chatbot" |
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with word3: |
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button3 = st.button("Langchain") |
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if button3: |
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self.query = "langchain" |
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with word4: |
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button4 = st.button("Extension") |
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if button4: |
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self.query = "extension" |
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with word5: |
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button5 = st.button("LLMs") |
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if button5: |
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self.query = "llms" |
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with word6: |
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button6 = st.button("API") |
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if button6: |
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self.query = "api" |
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def show_search_results(self): |
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if self.query or self.search_button: |
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st.write("#") |
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search_results = self.searcher.search(self.query, k=500) |
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num_search_results = len(search_results) |
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st.write(f"A total of {num_search_results} repositories found.") |
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if num_search_results > 0: |
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json_search_results = [] |
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for result in search_results: |
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docid = result.docid |
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doc = self.searcher.doc(docid) |
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print(docid) |
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print(doc) |
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print(doc.raw()) |
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json_data = json.loads(doc.raw()) |
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json_search_results.append(json_data) |
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for json_data in sorted( |
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json_search_results, key=lambda x: x["freq"], reverse=True |
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): |
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description = json_data["description"] |
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url = json_data["url"] |
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project_name = json_data["project_name"] |
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st.write("---") |
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st.subheader(f"[{project_name}]({url})") |
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st.write(description) |
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info = [] |
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language = json_data["language"] |
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if language is not None and len(language) > 0: |
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info.append(language) |
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else: |
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info.append("Laugage: Unkwown") |
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license = json_data["license"] |
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if license is None: |
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info.append("License: Unkwown") |
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else: |
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info.append(license) |
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st.caption(" / ".join(info)) |
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else: |
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if len(self.query) > 0: |
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scores = [] |
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for candidate_word in self.candidate_words: |
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score = difflib.SequenceMatcher( |
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None, self.query, candidate_word |
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).ratio() |
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scores.append(score) |
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num_candidate_words = 6 |
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indexes = np.argsort(scores)[::-1][:num_candidate_words] |
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suggestions = [self.candidate_words[i] for i in indexes] |
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suggestions = sorted( |
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set(suggestions), key=suggestions.index |
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) |
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st.caption("Suggestions") |
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for i, word in enumerate(suggestions, start=1): |
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st.write(f"{i}: {word}") |
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def main(): |
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SearchApplication() |
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if __name__ == "__main__": |
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main() |
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