Spaces:
Sleeping
Sleeping
File size: 3,307 Bytes
6013d45 |
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 87 88 89 90 91 92 |
from transformers import AutoTokenizer, AutoModelForCausalLM
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import torch
import json
from flask import Flask, request, jsonify
import gradio as gr
import os
# Load model XGLM
local_model_dir = "./local_model_bloomz"
if not os.path.exists(local_model_dir):
model_id = "bigscience/bloomz-560m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer.save_pretrained(local_model_dir)
model.save_pretrained(local_model_dir)
else:
tokenizer = AutoTokenizer.from_pretrained(local_model_dir, local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(local_model_dir, local_files_only=True)
# Load dataset function
def load_dataset():
with open('bps_dataset.json', 'r') as file:
data = json.load(file)
return data
dataset = load_dataset()
questions = dataset['questions']
answers = dataset['answers']
# Retrieval Function
def retrieve_answer(user_question):
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(questions + [user_question])
cosine_similarities = cosine_similarity(vectors[-1], vectors[:-1]).flatten()
best_match_idx = cosine_similarities.argmax()
return answers[best_match_idx]
# Generate response using XGLM
def generate_response(user_input, retrieved_answer):
input_text = f"{retrieved_answer}"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(
inputs['input_ids'],
max_length=300,
num_return_sequences=1,
temperature=0.7,
top_p=0.9,
top_k=50,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Flask API
app = Flask(__name__)
@app.route('/chatbot', methods=['POST'])
def chatbot():
data = request.json
user_input = data.get("message", "")
if user_input:
retrieved_answer = retrieve_answer(user_input)
response = generate_response(user_input, retrieved_answer)
return jsonify({"response": response, "retrieved_answer": retrieved_answer})
return jsonify({"response": "Mohon ajukan pertanyaan!"})
# Gradio Interface
def chatbot_response(user_input):
if user_input:
retrieved_answer = retrieve_answer(user_input)
response = generate_response(user_input, retrieved_answer)
return f"Chatbot: {response}\n\nRetrieved Answer: {retrieved_answer}"
def chat_interface(user_input, history=[]):
history.append(("Anda: " + user_input, "Chatbot: " + chatbot_response(user_input)))
return history, history
with gr.Blocks() as demo:
gr.Markdown("# Chatbot Sederhana dengan Gradio")
chatbot = gr.Chatbot(label="Chatbot")
with gr.Row():
txt_input = gr.Textbox(show_label=False, placeholder="Tulis pesan Anda di sini...")
submit_btn = gr.Button("Kirim")
submit_btn.click(chat_interface, inputs=[txt_input, chatbot], outputs=[chatbot, chatbot])
txt_input.submit(chat_interface, inputs=[txt_input, chatbot], outputs=[chatbot, chatbot])
# Jalankan Flask dan Gradio
if __name__ == '__main__':
demo.launch(server_name="0.0.0.0", server_port=7860)
app.run(port=5000)
|