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import gradio as gr
from huggingface_hub import InferenceClient
import chromadb
from chromadb.config import Settings
from chromadb import PersistentClient
# Set the correct path to the ChromaDB directory
client_db = PersistentClient(path="./chromadb_directory/chromadb_file")
# Load your collection
collection = client_db.get_collection("my_collection")
# Initialize the Hugging Face Inference Client
inference_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def retrieve_from_chromadb(query):
results = collection.query(query_text=query, n_results=5) # Adjust n_results as needed
return results['documents']
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare messages for the model
messages = [{"role": "system", "content": system_message}]
# Add conversation history
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Retrieve relevant documents from ChromaDB
retrieved_docs = retrieve_from_chromadb(message)
context = "\n".join(retrieved_docs) + "\nUser: " + message
messages.append({"role": "user", "content": context})
response = ""
# Generate response using the Inference Client
for message in inference_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
# Gradio Chat Interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", 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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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