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import streamlit as st |
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import random |
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import pandas as pd |
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import requests |
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from io import BytesIO |
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from PIL import Image |
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM |
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import re |
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import time |
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MAX_SIZE = (450, 450) |
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st.set_page_config(page_title="🔮 Divine Fortune Teller", page_icon=":crystal_ball:") |
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st.markdown( |
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""" |
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<style> |
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.reportview-container { |
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background: linear-gradient(135deg, #f6d365, #fda085); |
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} |
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.card { |
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background: rgba(255, 255, 255, 0.95); |
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padding: 30px; |
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border-radius: 12px; |
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box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1); |
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max-width: 800px; |
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margin: auto; |
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text-align: center; |
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} |
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/* Force all text to be black */ |
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body, input, textarea, .stMarkdown, label { |
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color: black !important; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
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if 'submitted' not in st.session_state: |
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st.session_state.submitted = False |
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if 'error_message' not in st.session_state: |
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st.session_state.error_message = "" |
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if 'question' not in st.session_state: |
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st.session_state.question = "" |
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if 'fortune_number' not in st.session_state: |
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st.session_state.fortune_number = None |
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if 'fortune_row' not in st.session_state: |
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st.session_state.fortune_row = None |
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if "button_count_temp" not in st.session_state: |
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st.session_state.button_count_temp = 0 |
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if "cfu_explain_text" not in st.session_state: |
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st.session_state.cfu_explain_text = "" |
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if "fortune_data" not in st.session_state: |
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try: |
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st.session_state.fortune_data = pd.read_csv("/home/user/app/resources/detail.csv") |
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except Exception as e: |
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st.error(f"Error loading CSV: {e}") |
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st.session_state.fortune_data = None |
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def load_and_resize_image(path, max_size=MAX_SIZE): |
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""" |
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Loads an image from a local file path and resizes it to fit within a specified maximum size. |
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""" |
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try: |
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img = Image.open(path) |
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img.thumbnail(max_size, Image.Resampling.LANCZOS) |
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return img |
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except Exception as e: |
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st.error(f"Error loading image: {e}") |
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return None |
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def download_and_resize_image(url, max_size=MAX_SIZE): |
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""" |
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Downloads an image from a given URL, then resizes it to a predefined maximum size. |
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""" |
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try: |
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response = requests.get(url) |
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response.raise_for_status() |
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image_bytes = BytesIO(response.content) |
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img = Image.open(image_bytes) |
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img.thumbnail(max_size, Image.Resampling.LANCZOS) |
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return img |
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except Exception as e: |
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st.error(f"Error loading image from URL: {e}") |
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return None |
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def display_text_field(label, text, height): |
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""" |
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Creates and displays a custom-styled text field with a title and scrollable content. |
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""" |
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html = f""" |
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<h6 style="display: block; margin-top: 10px;">{label}</h6> |
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<div style="border: 1px solid #ccc; border-radius: 4px; background-color: #f0f0f0; |
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padding: 10px; height: {height}px; overflow-y: auto; color: black; font-size: 16px;"> |
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<div>{text}</div> |
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</div> |
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""" |
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st.markdown(html, unsafe_allow_html=True) |
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def load_finetuned_classifier_model(question): |
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""" |
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Uses a fine-tuned text classification model to categorize the user's question into one of several predefined fortune themes. |
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""" |
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label_list = ["Geomancy", "Lost Property", "Personal Well-Being", "Future Prospect", "Traveling"] |
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mapping = {f"LABEL_{i}": label for i, label in enumerate(label_list)} |
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pipe = pipeline("text-classification", model="tonyhui2234/CustomModel_classifier_model_10") |
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prediction = pipe(question)[0]['label'] |
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predicted_label = mapping.get(prediction, prediction) |
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return predicted_label |
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def generate_answer(question, fortune): |
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""" |
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Generates a detailed explanation by feeding the question and the selected fortune text into a fine-tuned sequence-to-sequence language model. |
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""" |
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start_time = time.perf_counter() |
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tokenizer = AutoTokenizer.from_pretrained("tonyhui2234/finetuned_model_text_gen") |
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model = AutoModelForSeq2SeqLM.from_pretrained("tonyhui2234/finetuned_model_text_gen", device_map="auto") |
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input_text = "Question: " + question + " Fortune: " + fortune |
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inputs = tokenizer(input_text, return_tensors="pt", truncation=True) |
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outputs = model.generate( |
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**inputs, |
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max_length=256, |
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num_beams=4, |
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early_stopping=True, |
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repetition_penalty=2.0, |
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no_repeat_ngram_size=3 |
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) |
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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run_time = time.perf_counter() - start_time |
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print(f"Runtime: {run_time:.4f} seconds") |
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return answer |
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def analysis(row_detail, classifiy, question): |
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""" |
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Extracts a specific portion of the fortune details based on the classification result and then generates an answer using the text generation model. |
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""" |
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pattern = re.compile(re.escape(classifiy) + r":\s*(.*?)(?:\.|$)", re.IGNORECASE) |
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match = pattern.search(row_detail) |
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if match: |
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result = match.group(1) |
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return generate_answer(question, result) |
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else: |
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return "Heaven's secret cannot be revealed." |
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def check_sentence_is_english_model(question): |
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""" |
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Checks if the provided text is in English using a language detection model. |
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""" |
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pipe_english = pipeline("text-classification", model="eleldar/language-detection") |
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return pipe_english(question)[0]['label'] == 'en' |
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def check_sentence_is_question_model(question): |
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""" |
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Determines whether the input text is formulated as a question using a question vs. statement classifier. |
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""" |
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pipe_question = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier") |
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return pipe_question(question)[0]['label'] == 'LABEL_1' |
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def random_draw(): |
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""" |
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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. |
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""" |
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st.session_state.fortune_number = random.randint(1, 100) |
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df = st.session_state.fortune_data |
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if df is not None: |
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matching_row = df[df['CNumber'] == st.session_state.fortune_number] |
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if not matching_row.empty: |
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row = matching_row.iloc[0] |
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st.session_state.fortune_row = { |
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"Header": row.get("Header", "N/A"), |
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"Luck": row.get("Luck", "N/A"), |
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"Description": row.get("Description", "No description available."), |
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"Detail": row.get("Detail", "No detail available."), |
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"HeaderLink": row.get("link", None) |
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} |
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else: |
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st.session_state.fortune_row = { |
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"Header": "N/A", |
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"Luck": "N/A", |
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"Description": "No description available.", |
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"Detail": "No detail available.", |
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"HeaderLink": None |
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} |
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else: |
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st.session_state.error_message = "Fortune data is not available." |
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st.session_state.submitted = True |
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st.session_state.show_explain = False |
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def submit_callback(): |
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""" |
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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. |
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""" |
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question = st.session_state.get("question_input", "").strip() |
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if not question: |
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st.session_state.error_message = "Please enter a valid question." |
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st.session_state.submitted = False |
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return |
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if not check_sentence_is_english_model(question): |
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st.session_state.error_message = "Please enter in English!" |
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st.session_state.button_count_temp = 0 |
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return |
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if not check_sentence_is_question_model(question): |
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st.session_state.error_message = "This is not a question. Please enter again!" |
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st.session_state.button_count_temp = 0 |
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return |
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if st.session_state.button_count_temp == 0: |
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st.session_state.error_message = "Please take a moment to quietly reflect on your question in your mind, then click submit again!" |
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st.session_state.button_count_temp = 1 |
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return |
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st.session_state.error_message = "" |
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st.session_state.question = question |
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st.session_state.button_count_temp = 0 |
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random_draw() |
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def resubmit_callback(): |
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""" |
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Allows the user to submit a revised question with similar validations, then updates the fortune selection accordingly. |
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""" |
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new_question = st.session_state.get("resubmit_input", "").strip() |
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if new_question == "": |
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st.session_state.error_message = "Please enter a valid question." |
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return |
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if not check_sentence_is_english_model(new_question): |
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st.session_state.error_message = "Please enter in English!" |
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st.session_state.button_count_temp = 0 |
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return |
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if not check_sentence_is_question_model(new_question): |
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st.session_state.error_message = "This is not a question. Please enter again!" |
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st.session_state.button_count_temp = 0 |
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return |
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if st.session_state.button_count_temp == 0: |
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st.session_state.error_message = "Please take a moment to quietly reflect on your question in your mind, then click submit again!" |
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st.session_state.button_count_temp = 1 |
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return |
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st.session_state.error_message = "" |
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if new_question != st.session_state.question: |
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st.session_state.question = new_question |
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st.session_state.button_count_temp = 0 |
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random_draw() |
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def explain_callback(): |
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""" |
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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. |
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""" |
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question = st.session_state.question |
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if not st.session_state.fortune_row: |
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st.error("Fortune data is not available. Please submit your question first.") |
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return |
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row_detail = st.session_state.fortune_row.get("Detail", "No detail available.") |
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classify = load_finetuned_classifier_model(question) |
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print(f"classify Checking: {classify}\nQuestion: {question}") |
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cfu_explain = analysis(row_detail, classify, question) |
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st.session_state.cfu_explain_text = cfu_explain |
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st.session_state.show_explain = True |
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st.title("🔮 Divine Fortune Teller") |
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if not st.session_state.submitted: |
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st.image("/home/user/app/resources/front.png", use_container_width=True) |
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st.text_input("Ask your fortune question...", key="question_input") |
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st.button("Submit", on_click=submit_callback) |
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if st.session_state.error_message: |
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st.error(st.session_state.error_message) |
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else: |
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st.text_input("Your Question", value=st.session_state.question, key="resubmit_input") |
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st.button("Resubmit", on_click=resubmit_callback) |
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if st.session_state.error_message: |
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st.error(st.session_state.error_message) |
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col1, col2 = st.columns([2, 3]) |
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with col1: |
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if st.session_state.fortune_row and st.session_state.fortune_row.get("HeaderLink"): |
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img_from_url = download_and_resize_image(st.session_state.fortune_row.get("HeaderLink")) |
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if img_from_url: |
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st.markdown("<h6> </h6>", unsafe_allow_html=True) |
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st.image(img_from_url, use_container_width=False) |
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else: |
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default_img = load_and_resize_image("/home/user/app/resources/error.png") |
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if default_img: |
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st.image(default_img, caption="Default image", use_container_width=False) |
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else: |
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default_img = load_and_resize_image("/home/user/app/resources/error.png") |
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if default_img: |
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st.image(default_img, caption="Default image", use_container_width=False) |
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with col2: |
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if st.session_state.fortune_row: |
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luck_text = st.session_state.fortune_row.get("Luck", "N/A") |
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summary = f""" |
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<div style="font-size: 24px; font-weight: bold;"> |
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Fortune Stick Number: {st.session_state.fortune_number}<br> |
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Luck: {luck_text} |
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</div> |
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""" |
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st.markdown(summary, unsafe_allow_html=True) |
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description_text = st.session_state.fortune_row.get("Description", "No description available.") |
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detail_text = st.session_state.fortune_row.get("Detail", "No detail available.") |
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display_text_field("Description:", description_text, 180) |
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display_text_field("Detail:", detail_text, 180) |
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st.button("CFU Explain", on_click=explain_callback) |
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if st.session_state.show_explain: |
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display_text_field("Explanation:", st.session_state.cfu_explain_text, 200) |
