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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 | |
| # 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, batch_size=100): | |
| # Use dataset streaming | |
| dataset = load_dataset('Thefoodprocessor/recipe_new_with_features_full', split='train', streaming=True) | |
| # Process in batches | |
| texts = [] | |
| for i, example in enumerate(dataset): | |
| title = example['title_cleaned'] | |
| recipe = example['recipe_new'] | |
| meal_type = example['meal_type'] | |
| allergy = example['allergy_type'] | |
| ingredients_alternative = example['ingredients_alternatives'] | |
| # Concatenate the text from the columns | |
| text = f"{title} {recipe} {meal_type} {allergy} {ingredients_alternative}" | |
| texts.append(text) | |
| # Process the batch | |
| if (i + 1) % batch_size == 0: | |
| self._process_batch(texts, i) | |
| texts = [] | |
| # Process the remaining texts | |
| if texts: | |
| self._process_batch(texts, i) | |
| def _process_batch(self, texts, batch_start_idx): | |
| embeddings = self.embedding_model.encode(texts, batch_size=len(texts)).tolist() | |
| for j, embedding in enumerate(embeddings): | |
| self.collection.add(embeddings=[embedding], documents=[texts[j]], ids=[str(batch_start_idx + j)]) | |
| 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) | |
| # create a vector embedding | |
| vector_store = VectorStore("embedding_vector") | |
| vector_store.populate_vectors(dataset=None) | |
| # 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() | |