| 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 |
| import time |
|
|
| |
| MAX_SIZE = (450, 450) |
|
|
| st.set_page_config(page_title="🔮 Divine Fortune Teller", page_icon=":crystal_ball:") |
|
|
| |
| st.markdown( |
| """ |
| <style> |
| .reportview-container { |
| background: linear-gradient(135deg, #f6d365, #fda085); |
| } |
| .card { |
| background: rgba(255, 255, 255, 0.95); |
| padding: 30px; |
| border-radius: 12px; |
| box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1); |
| max-width: 800px; |
| margin: auto; |
| text-align: center; |
| } |
| /* Force all text to be black */ |
| body, input, textarea, .stMarkdown, label { |
| color: black !important; |
| } |
| </style> |
| """, |
| unsafe_allow_html=True, |
| ) |
|
|
| |
| if 'submitted' not in st.session_state: |
| st.session_state.submitted = False |
| if 'error_message' not in st.session_state: |
| st.session_state.error_message = "" |
| if 'question' not in st.session_state: |
| st.session_state.question = "" |
| 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 "button_count_temp" not in st.session_state: |
| st.session_state.button_count_temp = 0 |
| 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("/home/user/app/resources/detail.csv") |
| except Exception as e: |
| st.error(f"Error loading CSV: {e}") |
| st.session_state.fortune_data = None |
|
|
| |
| def load_and_resize_image(path, max_size=MAX_SIZE): |
| """ |
| Loads an image from a local file path and resizes it to fit within a specified maximum 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): |
| """ |
| Downloads an image from a given URL, then resizes it to a predefined maximum 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 display_text_field(label, text, height): |
| """ |
| Creates and displays a custom-styled text field with a title and scrollable content. |
| """ |
| html = f""" |
| <h6 style="display: block; margin-top: 10px;">{label}</h6> |
| <div style="border: 1px solid #ccc; border-radius: 4px; background-color: #f0f0f0; |
| padding: 10px; height: {height}px; overflow-y: auto; color: black; font-size: 16px;"> |
| <div>{text}</div> |
| </div> |
| """ |
| st.markdown(html, unsafe_allow_html=True) |
|
|
| |
| def load_finetuned_classifier_model(question): |
| """ |
| Uses a fine-tuned text classification model to categorize the user's question into one of several predefined fortune themes. |
| """ |
| label_list = ["Geomancy", "Lost Property", "Personal Well-Being", "Future Prospect", "Traveling"] |
| 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) |
| return predicted_label |
|
|
| def generate_answer(question, fortune): |
| """ |
| Generates a detailed explanation by feeding the question and the selected fortune text into a fine-tuned sequence-to-sequence language model. |
| """ |
| start_time = time.perf_counter() |
| tokenizer = AutoTokenizer.from_pretrained("tonyhui2234/finetuned_model_text_gen") |
| model = AutoModelForSeq2SeqLM.from_pretrained("tonyhui2234/finetuned_model_text_gen", device_map="auto") |
| 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) |
| run_time = time.perf_counter() - start_time |
| print(f"Runtime: {run_time:.4f} seconds") |
| return answer |
|
|
| def analysis(row_detail, classifiy, question): |
| """ |
| Extracts a specific portion of the fortune details based on the classification result and then generates an answer using the text generation model. |
| """ |
| pattern = re.compile(re.escape(classifiy) + r":\s*(.*?)(?:\.|$)", re.IGNORECASE) |
| match = pattern.search(row_detail) |
| if match: |
| result = match.group(1) |
| return generate_answer(question, result) |
| else: |
| return "Heaven's secret cannot be revealed." |
|
|
| def check_sentence_is_english_model(question): |
| """ |
| Checks if the provided text is in English using a language detection model. |
| """ |
| pipe_english = pipeline("text-classification", model="eleldar/language-detection") |
| return pipe_english(question)[0]['label'] == 'en' |
|
|
| def check_sentence_is_question_model(question): |
| """ |
| Determines whether the input text is formulated as a question using a question vs. statement classifier. |
| """ |
| pipe_question = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier") |
| return pipe_question(question)[0]['label'] == 'LABEL_1' |
|
|
| |
| def random_draw(): |
| """ |
| Randomly selects a fortune entry from the loaded CSV based on a randomly generated number and updates the session state with the fortune’s details. |
| """ |
| st.session_state.fortune_number = random.randint(1, 100) |
| df = st.session_state.fortune_data |
| 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] |
| 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.get("Detail", "No detail available."), |
| "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 |
| } |
| else: |
| st.session_state.error_message = "Fortune data is not available." |
| |
| st.session_state.submitted = True |
| st.session_state.show_explain = False |
|
|
| def submit_callback(): |
| """ |
| Validates the initial user input (ensuring it’s non-empty, in English, and a question), prompts the user to reflect, and then triggers a random fortune draw if the criteria are met. |
| """ |
| question = st.session_state.get("question_input", "").strip() |
| if not question: |
| st.session_state.error_message = "Please enter a valid question." |
| st.session_state.submitted = False |
| return |
| |
| 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 again!" |
| st.session_state.button_count_temp = 1 |
| return |
|
|
| st.session_state.error_message = "" |
| st.session_state.question = question |
| st.session_state.button_count_temp = 0 |
| random_draw() |
|
|
| def resubmit_callback(): |
| """ |
| Allows the user to submit a revised question with similar validations, then updates the fortune selection accordingly. |
| """ |
| new_question = st.session_state.get("resubmit_input", "").strip() |
| if new_question == "": |
| st.session_state.error_message = "Please enter a valid question." |
| return |
| |
| if not check_sentence_is_english_model(new_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(new_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 again!" |
| st.session_state.button_count_temp = 1 |
| return |
|
|
| st.session_state.error_message = "" |
| if new_question != st.session_state.question: |
| st.session_state.question = new_question |
| st.session_state.button_count_temp = 0 |
| random_draw() |
|
|
| def explain_callback(): |
| """ |
| Uses the selected fortune details and the classifier to generate and display a customized explanation for the user's question using the text generation model. |
| """ |
| question = st.session_state.question |
| 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.") |
| |
| classify = load_finetuned_classifier_model(question) |
| print(f"classify Checking: {classify}\nQuestion: {question}") |
| cfu_explain = analysis(row_detail, classify, question) |
| st.session_state.cfu_explain_text = cfu_explain |
| st.session_state.show_explain = True |
|
|
| |
| st.title("🔮 Divine Fortune Teller") |
|
|
| if not st.session_state.submitted: |
| st.image("/home/user/app/resources/front.png", use_container_width=True) |
| st.text_input("Ask your fortune question...", key="question_input") |
| st.button("Submit", on_click=submit_callback) |
| |
| if st.session_state.error_message: |
| st.error(st.session_state.error_message) |
| else: |
| st.text_input("Your Question", value=st.session_state.question, key="resubmit_input") |
| st.button("Resubmit", on_click=resubmit_callback) |
| if st.session_state.error_message: |
| st.error(st.session_state.error_message) |
| |
| col1, col2 = st.columns([2, 3]) |
| with col1: |
| if st.session_state.fortune_row and st.session_state.fortune_row.get("HeaderLink"): |
| img_from_url = download_and_resize_image(st.session_state.fortune_row.get("HeaderLink")) |
| if img_from_url: |
| st.markdown("<h6> </h6>", unsafe_allow_html=True) |
| st.image(img_from_url, use_container_width=False) |
| else: |
| default_img = load_and_resize_image("/home/user/app/resources/error.png") |
| if default_img: |
| st.image(default_img, caption="Default image", use_container_width=False) |
| else: |
| default_img = load_and_resize_image("/home/user/app/resources/error.png") |
| if default_img: |
| st.image(default_img, caption="Default image", use_container_width=False) |
| |
| with col2: |
| if st.session_state.fortune_row: |
| luck_text = st.session_state.fortune_row.get("Luck", "N/A") |
| summary = f""" |
| <div style="font-size: 24px; font-weight: bold;"> |
| Fortune Stick Number: {st.session_state.fortune_number}<br> |
| Luck: {luck_text} |
| </div> |
| """ |
| st.markdown(summary, unsafe_allow_html=True) |
| description_text = st.session_state.fortune_row.get("Description", "No description available.") |
| detail_text = st.session_state.fortune_row.get("Detail", "No detail available.") |
| |
| display_text_field("Description:", description_text, 180) |
| display_text_field("Detail:", detail_text, 180) |
| |
| st.button("CFU Explain", on_click=explain_callback) |
| if st.session_state.show_explain: |
| display_text_field("Explanation:", st.session_state.cfu_explain_text, 200) |
