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| # import packages | |
| __import__('pysqlite3') | |
| import sys | |
| sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') | |
| from sentence_transformers import SentenceTransformer | |
| import chromadb | |
| from datasets import load_dataset | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| import faiss | |
| # Embedding vector | |
| class VectorStore: | |
| def __init__(self, collection_name): | |
| # Initialize the embedding model | |
| self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1') | |
| self.chroma_client = chromadb.Client() | |
| self.collection = self.chroma_client.create_collection(name=collection_name) | |
| # Method to populate the vector store with embeddings from a dataset | |
| def populate_vectors(self, dataset): | |
| # Select the text columns to concatenate | |
| title = dataset['train']['title_cleaned'][:5000] # Limiting to 100 examples for the demo | |
| recipe = dataset['train']['recipe_new'][:5000] | |
| meal_type = dataset['train']['meal_type'][:5000] | |
| allergy = dataset['train']['allergy_type'][:5000] | |
| ingredients_alternative = dataset['train']['ingredients_alternatives'][:5000] | |
| # Concatenate the text from both columns | |
| texts = [f"{tit} {rep} {meal} {alle} {ingr} " for tit, rep, meal,alle, ingr in zip(title,recipe,meal_type,allergy,ingredients_alternative)] | |
| for i, item in enumerate(texts): | |
| embeddings = self.embedding_model.encode(item).tolist() | |
| self.collection.add(embeddings=[embeddings], documents=[item], ids=[str(i)]) | |
| # # Method to search the ChromaDB collection for relevant context based on a query | |
| def search_context(self, query, n_results=1): | |
| query_embeddings = self.embedding_model.encode(query).tolist() | |
| return self.collection.query(query_embeddings=query_embeddings, n_results=n_results) | |
| # importing dataset hosted on huggingface | |
| # dataset details - https://huggingface.co/datasets/Thefoodprocessor/recipe_new_with_features_full | |
| dataset = load_dataset('Thefoodprocessor/recipe_new_with_features_full') | |
| # create a vector embedding | |
| vector_store = VectorStore("embedding_vector") | |
| vector_store.populate_vectors(dataset) | |
| # Load the model and tokenizer | |
| # text generation model | |
| model_name = "meta-llama/Meta-Llama-3-8B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # Define the chatbot response function | |
| def chatbot_response(user_input): | |
| global conversation_history | |
| results = vector_store.search_context(user_input, n_results=1) | |
| context = results['documents'][0] if results['documents'] else "" | |
| conversation_history.append(f"User: {user_input}\nContext: {context[:150]}\nBot:") | |
| inputs = tokenizer("\n".join(conversation_history), return_tensors="pt") | |
| outputs = model.generate(**inputs, max_length=150, do_sample=True, temperature=0.7) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| conversation_history.append(response) | |
| return response | |
| # Gradio interface | |
| def chat(user_input): | |
| response = chatbot_response(user_input) | |
| return response | |
| iface = gr.Interface(fn=chat, inputs="text", outputs="text") | |
| iface.launch() | |