Update app.py

app.py

@@ -1,3 +1,4 @@
+import os
 import nltk
 from nltk.corpus import stopwords
 from nltk.stem import PorterStemmer
@@ -6,15 +7,26 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import gradio as gr
 
-# Download necessary NLTK data
+# Ensure necessary NLTK downloads
 nltk.download('punkt')
 nltk.download('stopwords')
 
-# Load customer inquiries dataset
-with open('my_text_file.txt', 'r') as f:
+# Path to the dataset file
+file_path = 'my_text_file.txt'
+
+# Check if the file exists
+if not os.path.exists(file_path):
+    raise FileNotFoundError(f"{file_path} not found in the environment.")
+
+# Load the dataset
+with open(file_path, 'r') as f:
     data = f.readlines()
 
-# Preprocess data
+# Ensure the data is loaded correctly
+if not data:
+    raise ValueError("The dataset is empty or could not be loaded properly.")
+
+# Preprocessing function for text
 def preprocess_text(text):
     tokens = word_tokenize(text.lower())
     stop_words = set(stopwords.words('english'))
@@ -23,27 +35,29 @@ def preprocess_text(text):
     stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]
     return stemmed_tokens
 
-# Create TF-IDF vectorizer
+# Create a TF-IDF vectorizer
 vectorizer = TfidfVectorizer(analyzer=preprocess_text)
 tfidf_matrix = vectorizer.fit_transform(data)
 
-# Define chatbot logic
+# Chatbot response function
 def chatbot_response(user_input):
     input_vector = vectorizer.transform([user_input])
     cosine_similarities = cosine_similarity(input_vector, tfidf_matrix)
     most_similar_index = cosine_similarities.argmax()
     return data[most_similar_index].strip()
 
-# Create Gradio interface
+# Gradio interface
 def chatbot_interface(user_input):
     response = chatbot_response(user_input)
     return response
 
+# Create a Gradio interface for the chatbot
 iface = gr.Interface(fn=chatbot_interface, 
                      inputs="text", 
                      outputs="text",
                      title="FAQ Chatbot",
-                     description="Enter a question to get a response from the chatbot based on the preloaded FAQ data.")
+                     description="Ask a question to the FAQ chatbot.")
 
+# Launch the Gradio app
 if __name__ == "__main__":
     iface.launch()