app.py

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

# Download NLTK resources
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('stopwords')

def preprocess(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 find_most_similar(sentence, candidates, threshold=0.15):
    input_bits = preprocess(sentence)
    chunks = [preprocess(candidate) for candidate in candidates]
    
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform([input_bits] + chunks)
    
    similarity_scores = cosine_similarity(vectors[0:1], vectors[1:]).flatten()
    
    similar_sentences = []
    for i, score in enumerate(similarity_scores):
        if score >= threshold:
            similar_sentences.append({"sentence": candidates[i], "f(score)": round(score, 4)})
    
    return similar_sentences

def read_sentences_from_file(file_location):
    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, sentence):
    file_location = file.name
    chunks = read_sentences_from_file(file_location)
    similar_sentences = find_most_similar(sentence, chunks, threshold=0.15)
    return json.dumps(similar_sentences, indent=4)

# Interface
file_uploader = gr.File(label="Upload a .txt file")
text_input = gr.Textbox(label="Enter a sentence")
output_text = gr.Textbox(label="RAG -QA")

iface = gr.Interface(
    fn=fetch_vectors,
    inputs=[file_uploader, text_input],
    outputs=output_text,
    title="Minimal RAG - For QA (Super Fast/Modeless)",
    description="Upload a text file and enter the question. The threshold is set to 0.15."
)

iface.launch(debug=True)