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| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| import openai | |
| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| # Initialize paths and model identifiers for easy configuration and maintenance | |
| filename = "output_topic_details.txt" # Path to the file storing destress-specific details | |
| retrieval_model_name = 'output/sentence-transformer-finetuned/' | |
| #openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = "You are a comfort chatbot specialized in providing information on therapy, destressing activites, and student opportunities." | |
| # Initial system message to set the behavior of the assistant | |
| messages = [{"role": "system", "content": system_message}] | |
| messages.append({ | |
| "role": "system", | |
| "content": "Do not use Markdown Format. Do not include hashtags or asterisks" | |
| }) | |
| # Attempt to load the necessary models and provide feedback on success or failure | |
| try: | |
| retrieval_model = SentenceTransformer(retrieval_model_name) | |
| print("Models loaded successfully.") | |
| except Exception as e: | |
| print(f"Failed to load models: {e}") | |
| def load_and_preprocess_text(filename): | |
| """ | |
| Load and preprocess text from a file, removing empty lines and stripping whitespace. | |
| """ | |
| try: | |
| with open(filename, 'r', encoding='utf-8') as file: | |
| segments = [line.strip() for line in file if line.strip()] | |
| print("Text loaded and preprocessed successfully.") | |
| return segments | |
| except Exception as e: | |
| print(f"Failed to load or preprocess text: {e}") | |
| return [] | |
| segments = load_and_preprocess_text(filename) | |
| def find_relevant_segment(user_query, segments): | |
| """ | |
| Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings. | |
| This version finds the best match based on the content of the query. | |
| """ | |
| try: | |
| # Lowercase the query for better matching | |
| lower_query = user_query.lower() | |
| # Encode the query and the segments | |
| query_embedding = retrieval_model.encode(lower_query) | |
| segment_embeddings = retrieval_model.encode(segments) | |
| # Compute cosine similarities between the query and the segments | |
| similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0] | |
| # Find the index of the most similar segment | |
| best_idx = similarities.argmax() | |
| # Return the most relevant segment | |
| return segments[best_idx] | |
| except Exception as e: | |
| print(f"Error in finding relevant segment: {e}") | |
| return "" | |
| def generate_response(user_query, relevant_segment): | |
| """ | |
| Generate a response emphasizing the bot's capability in providing therapy, destressing activites, and student opportunities information. | |
| """ | |
| try: | |
| user_message = f"Here's the information on your request: {relevant_segment}" | |
| # Append user's message to messages list | |
| messages.append({"role": "user", "content": user_message}) | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o", | |
| messages=messages, | |
| max_tokens=4000, | |
| temperature=0.5, | |
| top_p=1, | |
| frequency_penalty=0.5, | |
| presence_penalty=0.5, | |
| ) | |
| # Extract the response text | |
| output_text = response['choices'][0]['message']['content'].strip() | |
| # Append assistant's message to messages list for context | |
| messages.append({"role": "assistant", "content": output_text}) | |
| return output_text | |
| except Exception as e: | |
| print(f"Error in generating response: {e}") | |
| return f"Error in generating response: {e}" | |
| def query_model(question): | |
| """ | |
| Process a question, find relevant information, and generate a response. | |
| """ | |
| if question == "": | |
| return "Welcome to CalmConnect! Ask me anything about destressing strategies or student opportunities. Feel free to talk to our online therapist!" | |
| relevant_segment = find_relevant_segment(question, segments) | |
| if not relevant_segment: | |
| return "Could not find specific information. Please refine your question or head to our resources page." | |
| response = generate_response(question, relevant_segment) | |
| return response | |
| # Define the HTML iframe content | |
| iframe = ''' | |
| <iframe style="border-radius:12px" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vRroWVBXq1Fa0x7SvRTzSBMHFIp59VtVEWCxeg8kWJU4ll1_o4yzBnt4ArT88s7g4TQrMKEXZUQAeHF/pubhtml?widget=true&headers=false" width="100%" height="352" frameBorder="0" allowfullscreen="true" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe> | |
| ''' | |
| iframe2 = ''' | |
| <iframe style="border-radius:12px" src="https://open.spotify.com/embed/playlist/6wwxTePuIKYMqt6RCytB7X?utm_source=generator" width="100%" height="300" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe> | |
| ''' | |
| # Define the welcome message and specific topics the chatbot can provide information about | |
| welcome_message = """ | |
| # 🪷 Welcome to CalmConnect! 🪷 | |
| ## Your AI-driven assistant for destressing and extracurricular opportunity queries. Created by Olivia W, Alice T, and Cindy W of the 2024 Kode With Klossy CITY Camp. | |
| """ | |
| topics = """ | |
| ### If you are interested in the following below, click on our Student Opportunities Database! | |
| - Engineering | |
| - Technology / Computer Science | |
| - Research : STEM | |
| - Finance | |
| - Law / Political Science / Debate | |
| - The Arts | |
| - Business / Leadership | |
| - Pyschology | |
| - Medicine / Biology | |
| - Literature / Writing | |
| - College Prep | |
| - Advocacy: Non-Profit, Environment or Identity | |
| - Volunteering | |
| - Study Abroad | |
| """ | |
| topics2= """ | |
| ### Feel Free to ask CalmBot (Our Therapist Bot) anything from the topics below! | |
| - Arts and Crafts (When asking for arts and crafts ideas, state whether you have 15 min, 30 min, 45 min, 1 hour, 1 hour and a half, 2 hours, 2 hours and a half, 3 hours or greater) | |
| - Destressing strategies (Breathing Exercises, stretches, etc.) | |
| - Mental Health | |
| - Identity (Sexual, Gender, etc.) | |
| - Bullying | |
| - Racism | |
| - Relationships (Family, Friends, etc.) | |
| - Abuse (Emotional, Physical, Sexual, Mental, etc.) | |
| - Support Resources | |
| """ | |
| # Create a Gradio HTML component | |
| def display_iframe(): | |
| return iframe | |
| def display_iframe2(): | |
| return iframe2 | |
| theme = gr.themes.Default( | |
| primary_hue="neutral", | |
| secondary_hue="neutral", | |
| ).set( | |
| background_fill_primary='#e3e9da', | |
| background_fill_primary_dark='#e3e9da', | |
| background_fill_secondary="#f8f1ea", | |
| background_fill_secondary_dark="#f8f1ea", | |
| border_color_accent="#f8f1ea", | |
| border_color_accent_dark="#e3e9da", | |
| border_color_accent_subdued="#f8f1ea", | |
| border_color_primary="#f8f1ea", | |
| block_border_color="#f8f1ea", | |
| button_primary_background_fill="#f8f1ea", | |
| button_primary_background_fill_dark="#f8f1ea" | |
| ) | |
| # Setup the Gradio Blocks interface with custom layout components | |
| with gr.Blocks(theme='gstaff/xkcd') as demo: | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=10): | |
| gr.Markdown( | |
| """ | |
| # Theme preview: `xkcd` | |
| To use this theme, set `theme='gstaff/xkcd'` in `gr.Blocks()` or `gr.Interface()`. | |
| You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version | |
| of this theme. | |
| """ | |
| ) | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Image("CalmConnect.jpg", show_label = False, show_share_button = False, show_download_button = False) | |
| gr.Markdown(welcome_message) # Display the formatted welcome message | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(topics) # Show the topics on the left side | |
| gr.HTML(display_iframe()) # Embed the iframe on the left side | |
| gr.HTML(display_iframe2()) # Embed the iframe on the right side | |
| with gr.Column(): | |
| gr.Markdown(topics2) # Show the topics on the left side | |
| with gr.Row(): | |
| with gr.Column(): | |
| question = gr.Textbox(label="You", placeholder="What do you want to talk to CalmBot about?") | |
| answer = gr.Textbox(label="CalmBot's Response :D", placeholder="CalmBot will respond here..", interactive=False, lines=20) | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(fn=query_model, inputs=question, outputs=answer) | |
| with gr.Row(): | |
| big_block = gr.HTML("<button><a href='https://www.headspace.com/teens'>FREE: HEADSPACE FOR TEENS </a></button>") | |
| big_block2 = gr.HTML("<button><a href='https://calmconnect-flower.replit.app/'>PLAY FLOWER GAME</a></button>") | |
| big_block3 = gr.HTML("<button><a href='https://www.nyc.gov/site/doh/health/health-topics/teenspace.page'>NYC: TEENSPACE (free services)</a></button>") | |
| big_block4 =gr.HTML("<button><a href='https://www.teenlife.com/blog/mental-health-resources-for-teens/'>TEEN MENTAL HEALTH RESOURCES (free services)</a></button>") | |
| demo.launch() | |
| # Launch the Gradio app to allow user interaction | |
| demo.launch(share=True) |