Update app.py

app.py

@@ -7,6 +7,7 @@ from PIL import Image
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 import re
 
+# --------------------------- Configuration & Session State ---------------------------
 # Define maximum dimensions for the fortune image (in pixels)
 MAX_SIZE = (400, 400)
 
@@ -14,11 +15,11 @@ MAX_SIZE = (400, 400)
 if "button_count_temp" not in st.session_state:
     st.session_state.button_count_temp = 0
 
-# Set page configuration
+# Set page configuration and title
 st.set_page_config(page_title="Fortuen Stick Enquiry", layout="wide")
 st.title("Fortuen Stick Enquiry")
 
-# Initialize session state variables
+# Initialize session state variables for managing application state
 if "submitted_text" not in st.session_state:
     st.session_state.submitted_text = False
 if "fortune_number" not in st.session_state:
@@ -32,6 +33,7 @@ if "cfu_explain_text" not in st.session_state:
 if "stick_clicked" not in st.session_state:
     st.session_state.stick_clicked = False
 
+# Load fortune details from CSV file into session state
 if "fortune_data" not in st.session_state:
     try:
         st.session_state.fortune_data = pd.read_csv("/home/user/app/resources/detail.csv")
@@ -39,9 +41,11 @@ if "fortune_data" not in st.session_state:
         st.error(f"Error loading CSV: {e}")
         st.session_state.fortune_data = None
 
+# --------------------------- Model Functions ---------------------------
+# Function to load a fine-tuned classifier model and predict a label based on the question
 def load_finetuned_classifier_model(question):
     label_list = ["Geomancy", "Lost Property", "Personal Well-Being", "Future Prospect", "Traveling"]
-    # Create a mapping dictionary to convert the default "LABEL_x" output.
+    # Mapping to convert default "LABEL_x" outputs to meaningful labels
     mapping = {f"LABEL_{i}": label for i, label in enumerate(label_list)}
 
     pipe = pipeline("text-classification", model="tonyhui2234/CustomModel_classifier_model_10")
@@ -50,7 +54,7 @@ def load_finetuned_classifier_model(question):
     print(predicted_label)
     return predicted_label
 
-# Define your inference function
+# Function to generate a detailed answer by combining the user's question and the fortune detail
 def generate_answer(question, fortune):
     tokenizer = AutoTokenizer.from_pretrained("tonyhui2234/finetuned_model_text_gen")
     model = AutoModelForSeq2SeqLM.from_pretrained("tonyhui2234/finetuned_model_text_gen")
@@ -67,30 +71,36 @@ def generate_answer(question, fortune):
     answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return answer
 
+# Function that combines analysis with regex to extract the related fortune detail and then generate an answer
 def analysis(row_detail, classifiy, question):
-    # Use the classifier's output (e.g. "Personal Well-Being") in the regex.
+    # Use the classifier's output to match the corresponding detail in the fortune data
     pattern = re.compile(re.escape(classifiy) + r":\s*(.*?)(?:\.|$)", re.IGNORECASE)
     match = pattern.search(row_detail)
     if match:
         result = match.group(1)
-        # If you want to generate a custom answer, you can call generate_answer()
+        # Generate a custom answer based on the matched fortune detail and the user's question
         return generate_answer(question, result)
     else:
         return "Heaven's secret cannot be revealed."
 
+# Function to check if the input sentence is in English using a language detection model
 def check_sentence_is_english_model(question):
-    pipe_english = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+    pipe_english = pipeline("text-classification", model="eleldar/language-detection")
     return pipe_english(question)[0]['label'] == 'en'
 
+# Function to check if the input sentence is a question using a question vs. statement classifier
 def check_sentence_is_question_model(question):
     pipe_question = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier")
     return pipe_question(question)[0]['label'] == 'LABEL_1'
 
+# --------------------------- Callback Functions ---------------------------
+# Callback for when the submit button is clicked
 def submit_text_callback():
     question = st.session_state.get("user_sentence", "")
     # Clear any previous error message
     st.session_state.error_message = ""
     
+    # Validate that the input is in English and is a question
     if not check_sentence_is_english_model(question):
         st.session_state.error_message = "Please enter in English!"
         st.session_state.button_count_temp = 0
@@ -101,18 +111,20 @@ def submit_text_callback():
         st.session_state.button_count_temp = 0
         return
 
+    # Require a second confirmation click to proceed
     if st.session_state.button_count_temp == 0:
         st.session_state.error_message = "Please take a moment to quietly reflect on your question in your mind, then click submit again!"
         st.session_state.button_count_temp = 1
         return
 
+    # If validations pass, set submission flag and reset click counter
     st.session_state.submitted_text = True
-    st.session_state.button_count_temp = 0  # Reset the counter once submission is accepted
+    st.session_state.button_count_temp = 0
 
-    # Randomly generate a number from 1 to 100
+    # Randomly generate a fortune number between 1 and 100
     st.session_state.fortune_number = random.randint(1, 100)
     
-    # Look up the row in the CSV where CNumber matches the generated fortune number.
+    # Retrieve corresponding fortune details from the CSV based on the generated number
     df = st.session_state.fortune_data
     row_detail = ''
     if df is not None:
@@ -137,6 +149,7 @@ def submit_text_callback():
             }
     print(row_detail)
 
+# Function to load and resize a local image file
 def load_and_resize_image(path, max_size=MAX_SIZE):
     try:
         img = Image.open(path)
@@ -146,6 +159,7 @@ def load_and_resize_image(path, max_size=MAX_SIZE):
         st.error(f"Error loading image: {e}")
         return None
 
+# Function to download an image from a URL and resize it
 def download_and_resize_image(url, max_size=MAX_SIZE):
     try:
         response = requests.get(url)
@@ -158,49 +172,58 @@ def download_and_resize_image(url, max_size=MAX_SIZE):
         st.error(f"Error loading image from URL: {e}")
         return None
 
+# Callback for when the 'Cfu Explain' button is clicked
 def stick_enquiry_callback():
-    # Retrieve the user's question and the fortune detail
+    # Retrieve the user's question and ensure fortune data is available
     question = st.session_state.get("user_sentence", "")
     if not st.session_state.fortune_row:
         st.error("Fortune data is not available. Please submit your question first.")
         return
     row_detail = st.session_state.fortune_row.get("Detail", "No detail available.")
-    # Run the classifier model after the image has loaded
+    
+    # Classify the question to determine which fortune detail to use
     classifiy = load_finetuned_classifier_model(question)
-    # Generate the explanation using the analysis function
+    # Generate an explanation based on the classification and fortune detail
     cfu_explain = analysis(row_detail, classifiy, question)
-    # Save the returned value in session state for later display
+    # Save the generated explanation for display
     st.session_state.cfu_explain_text = cfu_explain
     st.session_state.stick_clicked = True
 
-# Main layout: Left (input) and Right (fortune display)
+# --------------------------- Layout & Display ---------------------------
+# Define the main layout with two columns: left for user input and right for fortune display
 left_col, _, right_col = st.columns([3, 1, 5])
 
-# ---- Left Column ----
+# ---- Left Column: User Input and Interaction ----
 with left_col:
     left_top = st.container()
     left_bottom = st.container()
+    
+    # Top container: Question input and submission button
     with left_top:
         st.text_area("Enter your question in English", key="user_sentence", height=150)
         st.button("submit", key="submit_button", on_click=submit_text_callback)
         if st.session_state.error_message:
             st.error(st.session_state.error_message)
+    
+    # Bottom container: Button to trigger explanation and display the generated answer
     if st.session_state.submitted_text:
         with left_bottom:
+            # Add spacing for better visual separation
             for _ in range(5):
                 st.write("")
             col1, col2, col3 = st.columns(3)
             with col2:
                 st.button("Cfu Explain", key="stick_button", on_click=stick_enquiry_callback)
             if st.session_state.stick_clicked:
-                # Display the explanation text saved from analysis()
+                # Display the generated explanation text
                 st.text_area(' ', value=st.session_state.cfu_explain_text, height=300, disabled=True)
 
-# ---- Right Column ----
+# ---- Right Column: Fortune Display (Image and Details) ----
 with right_col:
     with st.container():
         col_left, col_center, col_right = st.columns([1, 2, 1])
         with col_center:
+            # Display fortune image based on fortune data availability
             if st.session_state.submitted_text and st.session_state.fortune_row:
                 header_link = st.session_state.fortune_row.get("HeaderLink")
                 if header_link:
@@ -220,6 +243,7 @@ with right_col:
                 if img:
                     st.image(img, caption="Your Fortune", use_container_width=False)
     with st.container():
+        # Display fortune details: Number, Luck, Description, and Detail
         if st.session_state.fortune_row:
             luck_text = st.session_state.fortune_row.get("Luck", "N/A")
             description_text = st.session_state.fortune_row.get("Description", "No description available.")
@@ -234,4 +258,4 @@ with right_col:
             st.markdown(summary, unsafe_allow_html=True)
             
             st.text_area("Description", value=description_text, height=150, disabled=True)
-            st.text_area("Detail", value=detail_text, height=150, disabled=True)
+            st.text_area("Detail", value=detail_text, height=150, disabled=True)