Spaces:
Sleeping
Sleeping
| from huggingface_hub import InferenceClient | |
| #STEP1FROMSEMANTICSEARCH (import libraries) | |
| from sentence_transformers import SentenceTransformer | |
| import torch | |
| import gradio as gr | |
| import random | |
| client=InferenceClient("mistralai/Mistral-7B-Instruct-v0.2") | |
| #deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | |
| # Open the water_cycle.txt file in read mode with UTF-8 encoding - step 2 from semantic search | |
| with open("recipes.txt", "r", encoding="utf-8") as file: | |
| # Read the entire contents of the file and store it in a variable | |
| recipes_text = file.read() | |
| # Print the text below | |
| print(recipes_text) | |
| #Step 3 | |
| def preprocess_text(text): | |
| # Strip extra whitespace from the beginning and the end of the text | |
| cleaned_text = text.strip() | |
| # Split the cleaned_text by every newline character (\n) | |
| chunks = cleaned_text.split("[END]") | |
| # Create an empty list to store cleaned chunks | |
| cleaned_chunks = [] | |
| # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list | |
| for chunk in chunks: | |
| clean = chunk.strip() | |
| if len(chunk)>0: | |
| cleaned_chunks.append(clean) | |
| # Print cleaned_chunks | |
| print(cleaned_chunks) | |
| # Print the length of cleaned_chunks | |
| print(len(cleaned_chunks)) | |
| # Return the cleaned_chunks | |
| return cleaned_chunks | |
| # Call the preprocess_text function and store the result in a cleaned_chunks variable | |
| cleaned_chunks = preprocess_text(recipes_text) # Complete this line | |
| #Step 4 | |
| # Load the pre-trained embedding model that converts text to vectors | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| def create_embeddings(text_chunks): | |
| # Convert each text chunk into a vector embedding and store as a tensor | |
| chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the text_chunks list | |
| # Print the chunk embeddings | |
| print(chunk_embeddings) | |
| # Print the shape of chunk_embeddings | |
| print(chunk_embeddings.shape) | |
| # Return the chunk_embeddings | |
| return chunk_embeddings | |
| # Call the create_embeddings function and store the result in a new chunk_embeddings variable | |
| chunk_embeddings = create_embeddings(cleaned_chunks) # Complete this line | |
| #Step 5 | |
| # Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks | |
| def get_top_chunks(query, chunk_embeddings, text_chunks): | |
| # Convert the query text into a vector embedding | |
| query_embedding = model.encode(query,convert_to_tensor=True) # Complete this line | |
| # Normalize the query embedding to unit length for accurate similarity comparison | |
| query_embedding_normalized = query_embedding / query_embedding.norm() | |
| # Normalize all chunk embeddings to unit length for consistent comparison | |
| chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True) | |
| # Calculate cosine similarity between query and all chunks using matrix multiplication | |
| similarities = torch.matmul(chunk_embeddings_normalized,query_embedding_normalized) # Complete this line | |
| # Print the similarities | |
| print(similarities) | |
| # Find the indices of the 3 chunks with highest similarity scores | |
| top_indices = torch.topk(similarities, k=1).indices | |
| # Print the top indices | |
| print(top_indices) | |
| # Create an empty list to store the most relevant chunks | |
| top_chunks = [] | |
| # Loop through the top indices and retrieve the corresponding text chunks | |
| for i in top_indices: | |
| top_chunks.append(text_chunks[i]) | |
| print(top_chunks) | |
| # Return the list of most relevant chunks | |
| return top_chunks | |
| def respond(message, history, checkboxes, checkboxes2, checkboxes3): | |
| response = "" | |
| # Combine message + checkboxes into one search query | |
| query = message | |
| if checkboxes: | |
| query += " " + " ".join(checkboxes) | |
| if checkboxes2: | |
| query += " " + " ".join(checkboxes2) | |
| if checkboxes3: | |
| query += " " + " ".join(checkboxes3) | |
| print("FINAL SEMANTIC SEARCH QUERY:", query) | |
| best_recipes_chunk = get_top_chunks(query, chunk_embeddings, cleaned_chunks) | |
| print(best_recipes_chunk) | |
| str_recipes_chunk = "\n".join([str(chunk) for chunk in best_recipes_chunk]) | |
| print("RECIPES!!!!!!: " + str_recipes_chunk) | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "You are a helpful recipe assistant. You are only allowed to use the recipes listed below." | |
| f"Available Recipes:\n\n{str_recipes_chunk}\n\n(Use ONLY these to answer)" | |
| "You must NOT invent or guess any new recipes or ingredients. " | |
| f"The user wants something of {checkboxes} cuisine, and the meal should be for {checkboxes2} with {checkboxes3} dietary restriction." | |
| "Don't format the recipe as it is in the available recipes. Format your response like this:" | |
| "Here is a recipe that matches your needs: [recipe name]. It is [cuisine] cuisine. You can enjoy it for [time of day]. Its main ingredients are [core ingredients]. It fits a [dietary restriction] diet. This dish is [description]. To prepare it: [steps]" | |
| "Switch up the format a bit to make sure the responses are varied. Make sure you use proper grammar and don't have random capitals." | |
| "If the user’s question doesn’t match any of the recipes exactly or semantically, politely say: " | |
| "‘Sorry, I couldn’t find a match. Can you rephrase or ask about another dish?’" | |
| ) | |
| }, | |
| { | |
| "role": "user", | |
| "content": ( | |
| message | |
| ) | |
| }] | |
| if history: | |
| messages.extend(history) | |
| messages.append({"role":"user","content": message}) | |
| #shloka is cool | |
| response = client.chat_completion(messages, max_tokens = 700, temperature = 0.2, top_p = 0.3) | |
| #temperature and top_p control randomness | |
| return response['choices'][0]['message']['content'].strip() | |
| def vote(data: gr.LikeData): | |
| if data.liked: | |
| print("You upvoted this response: " + data.value["value"]) | |
| else: | |
| print("You downvoted this response: " + data.value["value"]) | |
| chat_theme = gr.themes.Monochrome( | |
| primary_hue = "orange", | |
| secondary_hue = "rose", | |
| neutral_hue = "rose").set( | |
| background_fill_primary = "*primary_50", | |
| input_background_fill = "*neutral_100", | |
| input_border_color_focus = "*neutral_100", | |
| button_secondary_background_fill = "*neutral_50", | |
| button_secondary_background_fill_hover = "*neutral_100") | |
| title = """# 🐑 NutriAssist 🌱""" | |
| with gr.Blocks(theme = chat_theme) as demo: | |
| # chatbot = gr.Chatbot(label="NutriAssist") | |
| # chatbot.like(vote, None, None) | |
| with gr.Row(scale=1): | |
| gr.Image( | |
| value="NutriAssistBanner.png", | |
| show_label=False, | |
| show_share_button = False, | |
| show_download_button = False) | |
| with gr.Row(scale=2): | |
| with gr.Column(scale=1): | |
| gr.Markdown(title) | |
| gr.Image( | |
| "NutriAssistInstructions.png", | |
| show_label = False, | |
| show_share_button = False, | |
| show_download_button = False) | |
| gr.Image( | |
| "NutriAssistTeam.png", | |
| show_label = False, | |
| show_share_button = False, | |
| show_download_button = False) | |
| with gr.Column(scale=2): | |
| with gr.Row(): | |
| # msg = gr.Textbox(placeholder="Ask about an item (e.g., banana peel)", label="Your Question") | |
| checkboxes = gr.CheckboxGroup( | |
| choices=["Thai", "Chinese", "Indian", "Mexican", "Italian"], | |
| label="Type of cuisine" | |
| ) | |
| with gr.Row(): | |
| # msg = gr.Textbox(placeholder="Ask about an item (e.g., banana peel)", label="Your Question") | |
| checkboxes2 = gr.CheckboxGroup( | |
| choices=["Breakfast", "Lunch", "Dinner", "Snack"], | |
| label="Time of day" | |
| ) | |
| with gr.Row(): | |
| # msg = gr.Textbox(placeholder="Ask about an item (e.g., banana peel)", label="Your Question") | |
| checkboxes3 = gr.CheckboxGroup( | |
| choices=["Vegetarian", "Vegan", "Dairy Free", "Gluten Free", "Nut Free"], | |
| label="Dietary Restriction" | |
| ) | |
| gr.ChatInterface(respond, additional_inputs=[checkboxes, checkboxes2, checkboxes3], type="messages") | |
| # send_btn = gr.Button("Send") | |
| # history_state = gr.State([]) | |
| # send_btn.click( | |
| # fn=respond, | |
| # # additional_inputs=[checkboxes, checkboxes2, checkboxes3], | |
| # outputs=[chatbot] | |
| # ) | |
| demo.launch() | |
| #chatbot = gr.ChatInterface(respond, type="messages") | |
| #chatbot.launch() |