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| import gradio as gr | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from nltk import word_tokenize | |
| from nltk.stem import WordNetLemmatizer | |
| from nltk.corpus import stopwords | |
| import nltk | |
| import json | |
| from typing import List, Dict, Any | |
| # Download NLTK resources | |
| nltk.download('punkt') | |
| nltk.download('wordnet') | |
| nltk.download('stopwords') | |
| def preprocess(sentence: str) -> str: | |
| """ | |
| Preprocesses a given sentence by converting to lowercase, tokenizing, lemmatizing, and removing stopwords. | |
| Parameters: | |
| sentence (str): The input sentence to be preprocessed. | |
| Returns: | |
| str: The preprocessed sentence. | |
| """ | |
| lemmatizer = WordNetLemmatizer() | |
| stop_words = set(stopwords.words('english')) | |
| tokens = word_tokenize(sentence.lower()) | |
| tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalnum()] | |
| tokens = [word for word in tokens if word not in stop_words] | |
| return ' '.join(tokens) | |
| def calculate_fx(sentence: str, candidates: List[str], threshold: float = 0.10) -> List[Dict[str, Any]]: | |
| """ | |
| Calculates the similarity scores between the input sentence and a list of candidate sentences. | |
| Parameters: | |
| sentence (str): The input sentence. | |
| candidates (List[str]): List of candidate sentences. | |
| threshold (float, optional): Threshold value for considering a sentence similar. Defaults to 0.15. | |
| Returns: | |
| List[Dict[str, Any]]: List of dictionaries containing similar sentences and their similarity scores. | |
| """ | |
| input_bits = preprocess(sentence) | |
| chunks = [preprocess(candidate) for candidate in candidates] | |
| vectorizer = TfidfVectorizer() | |
| vectors = vectorizer.fit_transform([input_bits] + chunks) | |
| f_scores = cosine_similarity(vectors[0:1], vectors[1:]).flatten() | |
| similar_chunks = [] | |
| for i, score in enumerate(f_scores): | |
| if score >= threshold: | |
| similar_chunks.append({"sentence": candidates[i], "f(score)": round(score, 4)}) | |
| return similar_chunks | |
| def read_sentences_from_file(file_location: str) -> List[str]: | |
| """ | |
| Reads sentences from a text file located at the given location. | |
| Parameters: | |
| file_location (str): Location of the text file. | |
| Returns: | |
| List[str]: List of sentences read from the file. | |
| """ | |
| with open(file_location, 'r') as file: | |
| text = file.read().replace('\n', ' ') | |
| sentences = [sentence.strip() for sentence in text.split('.') if sentence.strip()] | |
| return sentences | |
| def fetch_vectors(file: Any, sentence: str) -> str: | |
| """ | |
| Fetches similar sentences from a text file for a given input sentence. | |
| Parameters: | |
| file (Any): File uploaded by the user. | |
| sentence (str): Input sentence. | |
| Returns: | |
| str: JSON string containing similar sentences and their similarity scores. | |
| """ | |
| file_location = file.name | |
| chunks = read_sentences_from_file(file_location) | |
| similar_chunks = calculate_fx(sentence, chunks, threshold=0.15) | |
| return json.dumps(similar_chunks, indent=4) | |
| # Interface | |
| file_uploader = gr.File(label="Upload a .txt file") | |
| text_input = gr.Textbox(label="Enter question") | |
| output_text = gr.Textbox(label="Output") | |
| iface = gr.Interface( | |
| fn=fetch_vectors, | |
| inputs=[file_uploader, text_input], | |
| outputs=output_text, | |
| title="Minimal RAG - For QA (Super Fast/Modeless)", | |
| description="Fastest Minimal Rag for Question Answer, calculating cosine similarities and vectorizing using scikit-learn's TfidfVectorizer." | |
| ) | |
| iface.launch(debug=True) | |