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b9f2ace 6756a51 fb99966 b9f2ace ae4028b b9f9d33 c7d49e4 b9f9d33 ae4028b b9f2ace c7d49e4 b9f2ace c7d49e4 b9f2ace c7d49e4 b9f2ace c7d49e4 b9f2ace 9437ce0 b9f2ace | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | 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()
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