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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from sentence_transformers import SentenceTransformer | |
| import torch | |
| # Load knowledge | |
| with open("recipesplease.txt", "r", encoding="utf-8") as file: | |
| knowledge = file.read() | |
| cleaned_chunks = [chunk.strip() for chunk in knowledge.strip().split("\n") if chunk.strip()] | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True) | |
| def get_top_chunks(query): | |
| query_embedding = model.encode(query, convert_to_tensor=True) | |
| query_embedding_normalized = query_embedding / query_embedding.norm() | |
| similarities = torch.matmul(chunk_embeddings, query_embedding_normalized) | |
| top_indices = torch.topk(similarities, k=5).indices.tolist() | |
| return [cleaned_chunks[i] for i in top_indices] | |
| client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2") | |
| def respond(message, history, cuisine, dietary_restrictions, allergies): | |
| response = "" | |
| top_chunks = get_top_chunks(message) | |
| context = "\n".join(top_chunks) | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": f"You are a friendly recipe chatbot named BiteBot that responds to the user with any recipe from this: {context}. Find a recipe that is {cuisine} cuisine. They have the dietary restrictions,{dietary_restrictions} and are allergic to {allergies}. Return the title to the user and ask if this is the recipe they want. If they say yes return the recipe to the user, and if they say no provide another recipe." | |
| } | |
| ] | |
| if history: | |
| messages.extend(history) | |
| messages.append({"role": "user", "content": message}) | |
| stream = client.chat_completion( | |
| messages, | |
| max_tokens=300, | |
| temperature=1.2, | |
| stream=True, | |
| ) | |
| for message in stream: | |
| token = message.choices[0].delta.content | |
| if token is not None: | |
| response += token | |
| yield response | |
| theme = gr.themes.Monochrome( | |
| primary_hue="orange", | |
| secondary_hue="zinc", | |
| neutral_hue=gr.themes.Color(c100="rgba(255, 227.4411088400613, 206.9078947368421, 1)", c200="rgba(255, 229.53334184977007, 218.0921052631579, 1)", c300="rgba(255, 234.91658150229947, 213.6184210526316, 1)", c400="rgba(189.603125, 154.41663986650488, 133.88641721491229, 1)", c50="#f3d1bbff", c500="rgba(170.2125, 139.18781968574348, 118.70082236842106, 1)", c600="rgba(193.32187499999998, 129.35648241888094, 111.07528782894737, 1)", c700="rgba(184.13125000000002, 141.9707339039346, 106.60230263157897, 1)", c800="rgba(156.06796875, 104.12209005333418, 69.81988075657894, 1)", c900="rgba(156.39999999999998, 117.22008175779253, 80.2578947368421, 1)", c950="rgba(158.43203125, 125.1788770279765, 97.28282620614036, 1)"), | |
| text_size="sm", | |
| spacing_size="md", | |
| radius_size="sm", | |
| ).set( | |
| body_background_fill='*primary_50', | |
| body_background_fill_dark='*primary_50' | |
| ) | |
| with gr.Blocks(theme=theme) as chatbot: | |
| gr.Markdown("## 🥗🍴 The BiteBot") | |
| cuisine=gr.Textbox(label="cuisine") | |
| dietary_restrictions=gr.Dropdown(["Gluten-Free","Dairy-Free","Vegan","Vegetarian","Keto","Kosher","No Soy","No Seafood","No Pork","No Beef"], label="dietary restrictions") | |
| allergies=gr.Textbox(label="allergies") | |
| gr.ChatInterface( | |
| fn=respond, | |
| type="messages", additional_inputs=[cuisine,dietary_restrictions,allergies] | |
| ) | |
| chatbot.launch() | |