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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 chess-specific details | |
| retrieval_model_name = 'output/sentence-transformer-finetuned/' | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = "You are a mental music chatbot specialized in providing a curated music playlist according to a user's input." | |
| # 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 music recommendations from given Youtube links. | |
| """ | |
| try: | |
| user_message = f"Here's the information on music recommendations: {relevant_segment}" | |
| # Append user's message to messages list | |
| messages.append({"role": "user", "content": user_message}) | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=messages, | |
| max_tokens=300, | |
| temperature=0.2, | |
| top_p=1, | |
| frequency_penalty=0, | |
| presence_penalty=0 | |
| ) | |
| # 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 get_youtube_playlist(question): | |
| """ | |
| Check if the question contains a keyword and return the corresponding Youtube playlist link. | |
| """ | |
| keyword_links = { | |
| "sad": "Sorry that you feel this way, I hope this playlist will comfort you: https://www.youtube.com/playlist?list=PLPE5bssIbSTm3odLzXrlfCXnmpll8pqBu", | |
| "happy": "I'm so glad you feel this way, get hype to this upbeat playlist since you're feeling happy: https://www.youtube.com/playlist?list=PLPE5bssIbSTnsN_-gd3-2sgHxm7i4CSSS", | |
| "mad":"It's important to let your anger out in a healthy manner, listen to this playlist that will help you when you are feeling pressed:https://www.youtube.com/playlist?list=PLPE5bssIbSTnwU9lG7jdnuqzoKUKFbUGb", | |
| "chill": "Here is a playlist for a chill vibe: https://www.youtube.com/playlist?list=PLPE5bssIbSTkjkb4FfYcaI3tVtfE3IrAx", | |
| "delulu": "Here is a playlist for when you are yearning for someone or dealing with romance: https://www.youtube.com/playlist?list=PLPE5bssIbSTlfj8IbqgD2SRVh83Hs1TZG", | |
| "queen": "Yasss slayy here is a playlist for when you are feeling your best and are confident:https://www.youtube.com/playlist?list=PLPE5bssIbSTlPeSO5BbhKiQZ1RXVCz-id", | |
| "crazy": "It's okay to feel a little crazy sometimes, you're not alone, listen to this delulu playlist: https://www.youtube.com/playlist?list=PLPE5bssIbSTlfj8IbqgD2SRVh83Hs1TZG"} | |
| for keyword, link in keyword_links.items(): | |
| if keyword in question.lower(): | |
| return link | |
| # If no keyword matches, return None or handle accordingly | |
| return None | |
| def query_model(question): | |
| """ | |
| Process a question, find relevant information, and generate a response. | |
| """ | |
| youtube_playlist = get_youtube_playlist(question) | |
| if youtube_playlist: | |
| return f"Here is a link to a youtube song playlist based on how you are feeling! Just copy the link: {youtube_playlist}" | |
| if question == "": | |
| return "Welcome to Mental Music Bot! Ask me anything about music recommendations!" | |
| relevant_segment = find_relevant_segment(question, segments) | |
| if not relevant_segment: | |
| return "Could not find specific information. Please refine your question." | |
| response = generate_response(question, relevant_segment) | |
| return response | |
| # Define the welcome message and specific topics the chatbot can provide information about | |
| welcome_message = """ | |
| # 🎶 Welcome to Mental Music Bot! | |
| ## Your AI-driven friend for all music recommendations based off your emotions. Created by Sarem, Davina, and Brea of the 2024 Kode With Klossy DC Camp. | |
| """ | |
| topics = """ | |
| ### Feel Free to ask me anything from the topics below! | |
| - music recommendations for when you're feeling sad | |
| - music recommendations for when you're feeling happy | |
| - music recommendations for when you're feeling confident | |
| """ | |
| # Setup the Gradio Blocks interface with custom layout components | |
| with gr.Blocks(theme='freddyaboulton/dracula_revamped') as demo: | |
| gr.Image("https://huggingface.co/spaces/MentalMusicBot-1/MentalMusicBot/resolve/main/BDS%20mental%20music%20bot%20-%20KWK%20Pitch%20Party.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 | |
| with gr.Row(): | |
| with gr.Column(): | |
| question = gr.Textbox(label="Your question", placeholder="How are you feeling today?") | |
| answer = gr.Textbox(label="Mental Music Bot Response", placeholder="Mental Music Bot will respond here...", interactive=False, lines=10) | |
| submit_button = gr.Button("Submit") | |
| submit_button.click(fn=query_model, inputs=question, outputs=answer) | |
| # Launch the Gradio app to allow user interaction | |
| demo.launch(share=True) | |