import gradio as gr from huggingface_hub import InferenceClient from sentence_transformers import SentenceTransformer import torch import numpy as np client = InferenceClient('Qwen/Qwen2.5-72B-Instruct') # Define the theme for the app. theme_citrus = gr.themes.Citrus( primary_hue="green", secondary_hue="emerald", neutral_hue="lime", ) #sustainability tips SUSTAINABILITY_TIPS = [ "Bring your own: Carry a reusable water bottle, coffee cup, and shopping bag.", "Switch to LEDs: They use less energy and last longer than traditional bulbs.", "Reduce food waste: Plan meals, store food properly, and compost scraps.", "Choose sustainable transport: Walk, bike, carpool, or take public transit when possible.", "Buy less, choose quality: Opt for durable items over disposable ones.", "Unplug devices: Save energy by unplugging electronics when not in use.", "Go paperless: Switch to digital bills, receipts, and notes.", "Support eco-conscious brands: Choose companies with ethical sourcing and minimal packaging." ] # Open the ECOsphere.txt file in read mode with UTF-8 encoding with open("eskb.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable ECOsphere_text = file.read() def respond(message, history): top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks) print("Top results:", top_results) messages = [ {"role": "system", "content": f"You are a chatbot that encourages people to live more sustainably. Base your response on: {top_results}"} ] if history: messages.extend(history) messages.append({"role": "user", "content": message}) response = client.chat_completion( model="Qwen/Qwen2.5-7B-Instruct", messages=messages, max_tokens=200, temperature=0.5 ) return response.choices[0].message["content"].strip() cleaned_chunks = [] def preprocess_text(text): # Strip extra whitespace from the beginning and the end of the text cleaned_text = text.strip() chunks = cleaned_text.split('*') # 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: stripped_chunk = chunk.strip() cleaned_chunks.append(stripped_chunk) # Print cleaned_chunks print(cleaned_chunks) # Print the length of cleaned_chunks print(len(cleaned_chunks)) # Return the cleaned_chunks return cleaned_chunks cleaned_chunks = preprocess_text(ECOsphere_text) cleaned_chunks.extend(SUSTAINABILITY_TIPS) # 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 chunk_embeddings = create_embeddings(cleaned_chunks) # 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=5).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 indices in top_indices: relevant_info = cleaned_chunks[indices] top_chunks.append(relevant_info) return top_chunks # Define the function to change the font based on the dropdown selection. def change_font(font): if font == "Open dyslexic": gr.HTML('''