Update app.py

app.py

@@ -1,19 +1,226 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import streamlit as st
+import random
+import pandas as pd
+import requests
+from io import BytesIO
+from PIL import Image
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import re
 
-# Load the saved model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("/home/user/app/my_finetuned_model_2/")
-model = AutoModelForSeq2SeqLM.from_pretrained("/home/user/app/my_finetuned_model_2/")
+# Define maximum dimensions for the fortune image (in pixels)
+MAX_SIZE = (400, 400)
+
+# Initialize button click count in session state
+if "button_count_temp" not in st.session_state:
+    st.session_state.button_count_temp = 0
+
+# Set page configuration
+st.set_page_config(page_title="Fortuen Stick Enquiry", layout="wide")
+st.title("Fortuen Stick Enquiry")
+
+# Initialize session state variables
+if "submitted_text" not in st.session_state:
+    st.session_state.submitted_text = False
+if "fortune_number" not in st.session_state:
+    st.session_state.fortune_number = None
+if "fortune_row" not in st.session_state:
+    st.session_state.fortune_row = None
+if "error_message" not in st.session_state:
+    st.session_state.error_message = ""
+if "cfu_explain_text" not in st.session_state:
+    st.session_state.cfu_explain_text = ""
+
+if "fortune_data" not in st.session_state:
+    try:
+        st.session_state.fortune_data = pd.read_csv("detail.csv")
+    except Exception as e:
+        st.error(f"Error loading CSV: {e}")
+        st.session_state.fortune_data = None
+
+if "stick_clicked" not in st.session_state:
+    st.session_state.stick_clicked = False
+
+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 = {f"LABEL_{i}": label for i, label in enumerate(label_list)}
+
+    pipe = pipeline("text-classification", model="tonyhui2234/CustomModel_classifier_model_10")
+    prediction = pipe(question)[0]['label']
+    predicted_label = mapping.get(prediction, prediction)
+    print(predicted_label)
+    return predicted_label
 
 # Define your inference function
 def generate_answer(question, fortune):
+    # Load the saved model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("/home/user/app/my_finetuned_model_2/")
+    model = AutoModelForSeq2SeqLM.from_pretrained("/home/user/app/my_finetuned_model_2/")
+
     input_text = "Question: " + question + " Fortune: " + fortune
     inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
     outputs = model.generate(**inputs, max_length=256, num_beams=4, early_stopping=True, repetition_penalty=2.0, no_repeat_ngram_size=3)
     answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return answer
 
-# Test the model with a sample input
-sample_question = "Should I start my own business now?"
-sample_fortune = "absence of rain causes worry."
-print("Generated Answer:")
-print(generate_answer(sample_question, sample_fortune))
+def analysis(row_detail, classifiy, question):
+    # Use the classifier's output (e.g. "Personal Well-Being") in the regex.
+    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()
+        return generate_answer(question, result)
+        # return result
+    else:
+        return "Heaven's secret cannot be revealed."
+
+def check_sentence_is_english_model(question):
+    pipe_english = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+    return pipe_english(question)[0]['label'] == 'en'
+
+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'
+
+def submit_text_callback():
+    question = st.session_state.get("user_sentence", "")
+    # Clear any previous error message
+    st.session_state.error_message = ""
+    
+    if not check_sentence_is_english_model(question):
+        st.session_state.error_message = "Please enter in English!"
+        st.session_state.button_count_temp = 0
+        return
+
+    if not check_sentence_is_question_model(question):
+        st.session_state.error_message = "This is not a question. Please enter again!"
+        st.session_state.button_count_temp = 0
+        return
+
+    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."
+        st.session_state.button_count_temp = 1
+        return
+
+    st.session_state.submitted_text = True
+    st.session_state.button_count_temp = 0  # Reset the counter once submission is accepted
+
+    # Randomly generate a number from 1 to 100
+    st.session_state.fortune_number = random.randint(1, 100)
+    
+    # Look up the row in the CSV where CNumber matches the generated fortune number.
+    df = st.session_state.fortune_data
+    row_detail = ''
+    if df is not None:
+        matching_row = df[df['CNumber'] == st.session_state.fortune_number]
+        if not matching_row.empty:
+            row = matching_row.iloc[0]
+            row_detail = row.get("Detail", "No detail available.")
+            st.session_state.fortune_row = {
+                "Header": row.get("Header", "N/A"),
+                "Luck": row.get("Luck", "N/A"),
+                "Description": row.get("Description", "No description available."),
+                "Detail": row_detail,
+                "HeaderLink": row.get("link", None)
+            }
+        else:
+            st.session_state.fortune_row = {
+                "Header": "N/A",
+                "Luck": "N/A",
+                "Description": "No description available.",
+                "Detail": "No detail available.",
+                "HeaderLink": None
+            }
+    print(row_detail)
+    classifiy = load_finetuned_classifier_model(question)
+    cfu_explain = analysis(row_detail, classifiy, question)
+    # Save the returned value in session state for later display
+    st.session_state.cfu_explain_text = cfu_explain
+
+def load_and_resize_image(path, max_size=MAX_SIZE):
+    try:
+        img = Image.open(path)
+        img.thumbnail(max_size, Image.Resampling.LANCZOS)
+        return img
+    except Exception as e:
+        st.error(f"Error loading image: {e}")
+        return None
+
+def download_and_resize_image(url, max_size=MAX_SIZE):
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        image_bytes = BytesIO(response.content)
+        img = Image.open(image_bytes)
+        img.thumbnail(max_size, Image.Resampling.LANCZOS)
+        return img
+    except Exception as e:
+        st.error(f"Error loading image from URL: {e}")
+        return None
+
+def stick_enquiry_callback():
+    st.session_state.stick_clicked = True
+
+# Main layout: Left (input) and Right (fortune display)
+left_col, _, right_col = st.columns([3, 1, 5])
+
+# ---- Left Column ----
+with left_col:
+    left_top = st.container()
+    left_bottom = st.container()
+    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)
+    if st.session_state.submitted_text:
+        with left_bottom:
+            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()
+                st.text_area(' ', value=st.session_state.cfu_explain_text, height=300, disabled=True)
+
+# ---- Right Column ----
+with right_col:
+    with st.container():
+        col_left, col_center, col_right = st.columns([1, 2, 1])
+        with col_center:
+            if st.session_state.submitted_text and st.session_state.fortune_row:
+                header_link = st.session_state.fortune_row.get("HeaderLink")
+                if header_link:
+                    img_from_url = download_and_resize_image(header_link)
+                    if img_from_url:
+                        st.image(img_from_url, use_container_width=False)
+                    else:
+                        img = load_and_resize_image("/home/user/app/error.png")
+                        if img:
+                            st.image(img, use_container_width=False)
+                else:
+                    img = load_and_resize_image("/home/user/app/error.png")
+                    if img:
+                        st.image(img, use_container_width=False)
+            else:
+                img = load_and_resize_image("/home/user/app/fortune.png")
+                if img:
+                    st.image(img, caption="Your Fortune", use_container_width=False)
+    with st.container():
+        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.")
+            detail_text = st.session_state.fortune_row.get("Detail", "No detail available.")
+            
+            summary = f"""
+            <div style="font-size: 28px; font-weight: bold;">
+                Fortune stick number: {st.session_state.fortune_number}<br>
+                Luck: {luck_text}
+            </div>
+            """
+            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)