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}] # 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 = ''' ''' # 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 = """ ### Feel Free to ask CalmBot(Our Therapist Bot) anything from the topics below! - Arts and Crafts - 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 ### 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 """ # Create a Gradio HTML component def display_iframe(): return iframe theme = gr.themes.Monochrome( primary_hue="pink", secondary_hue="green", ).set( background_fill_primary='*primary_200', background_fill_primary_dark='*primary_200', background_fill_secondary='*secondary_300', background_fill_secondary_dark='*secondary_300', border_color_accent='*secondary_200', border_color_accent_dark='*secondary_600', border_color_accent_subdued='*secondary_200', border_color_primary='*secondary_300', block_border_color='*secondary_200', button_primary_background_fill='*secondary_300', button_primary_background_fill_dark='*secondary_300') # Setup the Gradio Blocks interface with custom layout components with gr.Blocks(theme=theme) as demo: theme='gstaff/xkcd' 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 with gr.Row(): with gr.Column(): question = gr.Textbox(label="Your Request", placeholder="What would you like to talk about?") answer = gr.Textbox(label="CalmBot's Response", placeholder="CalmBot will respond here...", interactive=False, lines=17) submit_button = gr.Button("Submit") submit_button.click(fn=query_model, inputs=question, outputs=answer) demo.launch() # Launch the Gradio app to allow user interaction demo.launch(share=True)