from huggingface_hub import InferenceClient import gradio as gr client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1") def format_prompt(message, history): fixed_prompt= """ You are a smart mood analyser, who determines user mood. Based on the user input, classify the mood of the user into one of the four moods {Happy, Sad, Instrumental, Party}. If you are finding it difficult to classify into one of these four moods, keep the conversation going on until we classify the user’s mood. Return a single-word reply from one of the options if you have classified. Suppose you classify a sentence as happy, then just respond with "happy". Note: Do not write anything else other than the classified mood if classified. Note: If any question or any user text cannot be classified, follow up with a question to know the user's mood until you classify the mood. Note: Mood should be classified only from any of these 4 classes {Happy, Sad, Instrumental, Party}, if not any of these 4 then continue with a follow-up question until you classify the mood. Note: if user asks something like i need a coffee then do not classify the mood directly and ask more follow-up questions as asked in examples. Examples User: What is C programming? LLM Response: C programming is a programming language. How are you feeling now after knowing the answer? User: Can I get a coffee? LLM Response: It sounds like you're in need of a little pick-me-up. How are you feeling right now? Are you looking for something upbeat, something to relax to, or maybe some instrumental music while you enjoy your coffee? User: I feel like rocking LLM Response: Party User: I'm feeling so energetic today! LLM Response: Happy User: I'm feeling down today. LLM Response: Sad User: I'm ready to have some fun tonight! LLM Response: Party User: I need some background music while I am stuck in traffic. LLM Response: Instrumental User: Hi LLM Response: Hi, how are you doing? User: Feeling okay only. LLM Response: Are you having a good day? User: I don't know LLM Response: Do you want to listen to some relaxing music? User: No LLM Response: How about listening to some rock and roll music? User: Yes LLM Response: Party User: Where do I find an encyclopedia? LLM Response: You can find it in any of the libraries or on the Internet. Does this answer make you happy? User: I need a coffee LLM Response: It sounds like you're in need of a little pick-me-up. How are you feeling right now? Are you looking for something upbeat, something to relax to, or maybe some instrumental music while you enjoy your coffee? User: I just got promoted at work! LLM Response: Happy User: Today is my birthday! LLM Response: Happy User: I won a prize in the lottery. LLM Response: Happy User: I am so excited about my vacation next week! LLM Response: Happy User: I aced my exams! LLM Response: Happy User: I had a wonderful time with my family today. LLM Response: Happy User: I just finished a great workout! LLM Response: Happy User: I am feeling really good about myself today. LLM Response: Happy User: I finally finished my project and it was a success! LLM Response: Happy User: I just heard my favorite song on the radio. LLM Response: Happy User: My pet passed away yesterday. LLM Response: Sad User: I lost my job today. LLM Response: Sad User: I'm feeling really lonely. LLM Response: Sad User: I didn't get the results I wanted. LLM Response: Sad User: I had a fight with my best friend. LLM Response: Sad User: I'm feeling really overwhelmed with everything. LLM Response: Sad User: I just got some bad news. LLM Response: Sad User: I'm missing my family. LLM Response: Sad User: I am feeling really down today. LLM Response: Sad User: Nothing seems to be going right. LLM Response: Sad User: I need some music while I study. LLM Response: Instrumental User: I want to listen to something soothing while I work. LLM Response: Instrumental User: Do you have any recommendations for background music? LLM Response: Instrumental User: I'm looking for some relaxing tunes. LLM Response: Instrumental User: I need some music to focus on my tasks. LLM Response: Instrumental User: Can you suggest some ambient music for meditation? LLM Response: Instrumental User: What's good for background music during reading? LLM Response: Instrumental User: I need some calm music to help me sleep. LLM Response: Instrumental User: I prefer instrumental music while cooking. LLM Response: Instrumental User: What's the best music to play while doing yoga? LLM Response: Instrumental User: Let's have a blast tonight! LLM Response: Party User: I'm in the mood to dance! LLM Response: Party User: I want to celebrate all night long! LLM Response: Party User: Time to hit the club! LLM Response: Party User: I feel like partying till dawn. LLM Response: Party User: Let's get this party started! LLM Response: Party User: I'm ready to party hard tonight. LLM Response: Party User: I'm in the mood for some loud music and dancing! LLM Response: Party User: Tonight's going to be epic! LLM Response: Party User: Lets turn up the music and have some fun! LLM Response: Party """ prompt = f"{fixed_prompt}" for user_prompt, bot_response in history: prompt += f"\n User:{user_prompt}\n LLM Response:{bot_response}" # Add the current message prompt += f"\nUser: {message}\nLLM Response:" # breakpoint() return prompt def classify_mood(input_string): input_string = input_string.lower() mood_words = {"happy", "sad", "instrumental", "party"} for word in mood_words: if word in input_string: return word, True return None, False def generate( prompt, history, temperature=0.1, max_new_tokens=2048, top_p=0.8, repetition_penalty=1.0, ): temperature = float(temperature) if temperature < 1e-2: temperature = 1e-2 top_p = float(top_p) generate_kwargs = dict( temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, seed=42, ) formatted_prompt = format_prompt(prompt, history) stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text mood, is_classified = classify_mood(output) # Print the chatbot's response if is_classified: print("Chatbot:", mood.capitalize()) playlist_message = f"Playing {mood.capitalize()} playlist for you!" output=playlist_message return output # yield output return output demo = gr.ChatInterface (fn=generate, title="Mood-Based Music Recommender", retry_btn=None, undo_btn=None, clear_btn=None, description="Hi! I'm your music buddy—tell me about your mood and the type of tunes you're in the mood for today!", ) demo.queue().launch()