Spaces:
No application file
No application file
File size: 3,047 Bytes
297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab decda19 297e1ab |
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 |
import gradio as gr
import torch
from transformers import AutoTokenizer, pipeline
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
# Configuration
class Config:
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
embedding_model = "all-MiniLM-L6-v2"
vector_dim = 384
top_k = 3
chunk_size = 256
# Vector Database
class VectorDB:
def __init__(self):
self.index = faiss.IndexFlatL2(Config.vector_dim)
self.texts = []
self.embedding_model = SentenceTransformer(Config.embedding_model)
def add_text(self, text: str):
embedding = self.embedding_model.encode([text])[0]
embedding = np.array([embedding], dtype=np.float32)
faiss.normalize_L2(embedding)
self.index.add(embedding)
self.texts.append(text)
def search(self, query: str):
if self.index.ntotal == 0:
return []
query_embedding = self.embedding_model.encode([query])[0]
query_embedding = np.array([query_embedding], dtype=np.float32)
faiss.normalize_L2(query_embedding)
D, I = self.index.search(query_embedding, min(Config.top_k, self.index.ntotal))
return [self.texts[i] for i in I[0] if i < len(self.texts)]
# Load Model
class TinyChatModel:
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained(Config.model_name)
self.pipe = pipeline("text-generation", model=Config.model_name, torch_dtype=torch.bfloat16, device_map="auto")
def generate_response(self, message: str, context: str = ""):
messages = [{"role": "user", "content": message}]
if context:
messages.insert(0, {"role": "system", "content": f"Context:\n{context}"})
prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = self.pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
return outputs[0]["generated_text"].split("<|assistant|>")[-1].strip()
# Initialize
vector_db = VectorDB()
chat_model = TinyChatModel()
def chat_interface(user_input):
context = "\n".join(vector_db.search(user_input))
response = chat_model.generate_response(user_input, context)
vector_db.add_text(f"User: {user_input}\nAssistant: {response}")
return response
def add_text_interface(text):
vector_db.add_text(text)
return "Text added to memory!"
# Gradio UI
demo = gr.Blocks()
with demo:
gr.Markdown("# 🦙 TinyChat - AI Chatbot")
with gr.Row():
chatbot = gr.Chatbot()
with gr.Row():
user_input = gr.Textbox(label="Your Message")
send_btn = gr.Button("Send")
with gr.Row():
add_text_input = gr.Textbox(label="Add Knowledge to AI")
add_text_btn = gr.Button("Add Text")
send_btn.click(chat_interface, inputs=user_input, outputs=chatbot)
add_text_btn.click(add_text_interface, inputs=add_text_input, outputs=gr.Textbox())
# Launch
if __name__ == "__main__":
demo.launch()
|