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| import gradio as gr | |
| import faiss | |
| import json | |
| import numpy as np | |
| from sentence_transformers import SentenceTransformer | |
| from huggingface_hub import InferenceClient | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| # Load Uber FAQ Data | |
| with open("uber_faqs.json", "r") as f: | |
| faq_data = json.load(f) | |
| faq_questions = [item["question"] for item in faq_data] | |
| faq_answers = {item["question"]: item["answer"] for item in faq_data} | |
| # Load Sentence Transformer Model for Embeddings | |
| embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") | |
| faq_embeddings = embedding_model.encode(faq_questions, convert_to_numpy=True) | |
| # Create FAISS Index | |
| index = faiss.IndexFlatL2(faq_embeddings.shape[1]) | |
| index.add(faq_embeddings) | |
| def retrieve_uber_info(query): | |
| """Retrieve the most relevant Uber FAQ answer for the given query.""" | |
| query_embedding = embedding_model.encode([query], convert_to_numpy=True) | |
| D, I = index.search(query_embedding, k=1) # Get the closest match | |
| retrieved_question = faq_questions[I[0][0]] | |
| retrieved_answer = faq_answers[retrieved_question] | |
| return retrieved_answer | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| """Generate a response using Zephyr 7B while integrating retrieved Uber knowledge.""" | |
| retrieved_answer = retrieve_uber_info(message) | |
| system_message += f"\n\nUber FAQ Context: {retrieved_answer}" | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are an Uber AI assistant. Only answer questions about Uber services, policies, pricing, and support. If a question is unrelated to Uber, say 'I can only help with Uber-related topics.'", label="System Instruction"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |