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import gradio as gr
from huggingface_hub import InferenceClient
!pip install -q sentence-transformers
from sentence_transformers import SentenceTransformer
import torch
with open("knowlege.txt", "r", encoding="utf-8") as file:
knowlege = file.read()
print(knowlege)
cleaned_chunks = [chunk.strip() for chunk in knowlege.strip().split("\n") if chunk.strip()]
print(cleaned_chunks)
model = SentenceTransformer('all-MiniLM-L6-v2')
chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
print(chunk_embeddings)
cleaned_text = ""
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)
print(similarities)
top_indices = torch.topk(similarities, k=5).indices.tolist()
print(top_indices)
return [cleaned_chunks[i] for i in top_indices]
top_results = get_top_chunks("What are some good wizard characters?")
print(top_results)
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def respond(message, history):
# response = ""
# messages = [{"role": "system", "content": "You are a chatbot that helps users create characters for role playing games."}]
# if history:
# messages.extend(history)
# messages.append({"role": "user", "content": message})
# stream = client.chat_completion(
# messages,
# max_tokens=100,
# temperature=1.2,
# stream=True
# )
# for message in stream:
# token = message.choices[0].delta.content
# if token is not None:
# response += token
# yield response
def respond(message, history):
response = ""
# Retrieve top chunks based on the current user message
top_chunks = get_top_chunks(message)
context = "\n".join(top_chunks)
# Add knowledge as part of system instructions
messages = [
{
"role": "system",
"content": (
"You are a chatbot that helps users create characters for role-playing games. "
"Use the following knowledge to inform your answers:\n\n" + context
)
}
]
if history:
messages.extend(history)
messages.append({"role": "user", "content": message})
stream = client.chat_completion(
messages,
max_tokens=100,
temperature=1.2,
stream=True
)
for message in stream:
token = message.choices[0].delta.content
if token is not None:
response += token
yield response
chatbot = gr.ChatInterface(respond, type="messages")
chatbot.launch()
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