Update app.py

app.py

@@ -6,13 +6,23 @@ 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):
+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())
@@ -20,45 +30,75 @@ def preprocess(sentence):
     tokens = [word for word in tokens if word not in stop_words]
     return ' '.join(tokens)
 
-def find_most_similar(sentence, candidates, threshold=0.15):
+def calculate_fx(sentence: str, candidates: List[str], threshold: float = 0.15) -> 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)
     
-    similarity_scores = cosine_similarity(vectors[0:1], vectors[1:]).flatten()
+    f_scores = cosine_similarity(vectors[0:1], vectors[1:]).flatten()
     
-    similar_sentences = []
-    for i, score in enumerate(similarity_scores):
+    similar_chunks = []
+    for i, score in enumerate(f_scores):
         if score >= threshold:
-            similar_sentences.append({"sentence": candidates[i], "f(score)": round(score, 4)})
+            similar_chunks.append({"sentence": candidates[i], "f(score)": round(score, 4)})
     
-    return similar_sentences
+    return similar_chunks
 
-def read_sentences_from_file(file_location):
+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, sentence):
+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_sentences = find_most_similar(sentence, chunks, threshold=0.15)
-    return json.dumps(similar_sentences, indent=4)
+    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 a sentence")
-output_text = gr.Textbox(label="RAG -QA")
+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="Upload a text file and enter the question. The threshold is set to 0.15."
+    description="Fastest Minimal Rag for Question Answer, calculating cosine similarities and vectorizing using scikit-learn's TfidfVectorizer."
 )
 
-iface.launch(debug=True)
+iface.launch(debug=True)