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04192d0 b6ce12d 04192d0 b6ce12d 04192d0 b6ce12d 04192d0 b6ce12d 04192d0 b6ce12d 04192d0 b6ce12d 04192d0 b6ce12d 04192d0 | 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 | import gradio as gr
import faiss
import pickle
from sentence_transformers import SentenceTransformer
import numpy as np
from huggingface_hub import InferenceClient
index = faiss.read_index("alzheimers_index.faiss")
with open("chunks.pkl", "rb") as f:
chunks = pickle.load(f)
model = SentenceTransformer("all-MiniLM-L6-v2")
def retrieve_rag_context(query, k=3):
"""Return top-k relevant chunks for a query."""
query_embedding = model.encode([query])
distances, indices = index.search(np.array(query_embedding), k)
results = "\n\n---\n\n".join([chunks[i]["text"] for i in indices[0]])
return results
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""Respond using GPT-OSS-20B with RAG context"""
client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
# Retrieve RAG context
rag_context = retrieve_rag_context(message)
# Combine system message with RAG context
full_system_message = f"{system_message}\n\nRelevant info from knowledge base:\n{rag_context}"
# Prepare messages
messages = [{"role": "system", "content": full_system_message}]
messages.extend(history)
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,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = 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
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a helpful AI assistant for Alzheimer's patients and caregivers.", label="System message"),
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)"),
],
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch()
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