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import gradio as gr
from huggingface_hub import InferenceClient
import os
import faiss
from transformers import pipeline
from sentence_transformers import SentenceTransformer
documents = [
"The capital of France is Paris.",
"Python is a popular programming language.",
"The Eiffel Tower is located in Paris.",
"Llama is a type of animal found in South America.",
"Paris is known for its art, fashion, and culture.",
"Gabor Toth is the author of this document."
]
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
document_embeddings = embedding_model.encode(documents, convert_to_tensor=True)
document_embeddings_np = document_embeddings.cpu().numpy()
index = faiss.IndexFlatL2(document_embeddings_np.shape[1])
index.add(document_embeddings_np)
client = InferenceClient("meta-llama/Llama-3.2-B-Instruct")
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
query_embedding = embedding_model.encode([message], convert_to_tensor=True)
query_embedding_np = query_embedding.cpu().numpy()
distances, indices = index.search(query_embedding_np, k=1)
relevant_document = documents[indices[0][0]]
messages = [{"role": "system", "content": system_message},{{"role": "system", "content": f"context: {relevant_document}"}}]
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
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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