Spaces:
Runtime error
Runtime error
File size: 6,634 Bytes
aa591fb 0ed7157 aa591fb |
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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
!pip uninstall accelerate peft bitsandbytes transformers trl -y
!pip install accelerate peft==0.13.2 bitsandbytes transformers trl==0.12.0
!pip install huggingface_hub
!pip install ipywidgets
!pip install -q gradio
import torch
from trl import SFTTrainer
from peft import LoraConfig
from datasets import load_dataset
from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, pipeline)
import ipywidgets as widgets
from IPython.display import display
import gradio as gr
llama_model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path = "aboonaji/llama2finetune-v2",
quantization_config = BitsAndBytesConfig(load_in_4bit = True, bnb_4bit_compute_dtype = getattr(torch, "float16"), bnb_4bit_quant_type = "nf4"))
llama_model.config.use_cache = False
llama_model.config.pretraining_tp = 1
llama_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "aboonaji/llama2finetune-v2", trust_remote_code = True)
llama_tokenizer.pad_token = llama_tokenizer.eos_token
llama_tokenizer.padding_side = "right"
training_arguments = TrainingArguments(output_dir = "./results", per_device_train_batch_size = 1, max_steps = 100)
llama_sft_trainer = SFTTrainer(model = llama_model,
args = training_arguments,
train_dataset = load_dataset(path = "aboonaji/wiki_medical_terms_llam2_format", split = "train"),
tokenizer = llama_tokenizer,
peft_config = LoraConfig(task_type = "CAUSAL_LM", r = 16, lora_alpha = 16, lora_dropout = 0.1),
dataset_text_field = "text")
llama_sft_trainer.train()
generator = pipeline("text-generation", model=llama_model, tokenizer=llama_tokenizer, max_length=500)
# In-memory user database
user_db = {}
# Response function
def generate_response(prompt):
response = generator(f"<s>[INST] {prompt} [/INST]")[0]["generated_text"]
return response
# Sign-up and login logic
def signup_user(new_username, new_password):
if not new_username or not new_password:
return "❌ No input. Please provide both username and password."
if new_username in user_db:
return "❌ Username already exists."
user_db[new_username] = new_password
return "✅ Account created! Please log in."
def login_user(username, password):
if username in user_db and user_db[username] == password:
return (
gr.update(visible=True), # Show chat UI
gr.update(visible=False), # Hide login UI
"", # Clear login message
gr.update(selected=3) # Switch to Chat tab
)
return (
gr.update(visible=False),
gr.update(visible=True),
"❌ Invalid credentials",
gr.update(selected=2)
)
def logout_user():
return (
gr.update(visible=False), # Hide chat UI
gr.update(visible=True), # Show login UI
gr.update(selected=0) # Switch to Landing tab
)
with gr.Blocks(theme="soft", css="""
#create-btn button,
#login-btn button,
#submit-btn button,
#logout-btn button {
font-size: 12px !important;
padding: 4px 8px !important;
height: 30px !important;
width: auto !important;
min-width: 80px !important;
}
""") as demo:
with gr.Tabs(selected=0, elem_id="tabs") as tabs:
with gr.Tab("Home"):
with gr.Column(elem_id="landing-container") as landing_ui:
gr.Markdown("# 🏠 Welcome to MEDChat AI")
gr.Markdown("---")
gr.Markdown("#### 🔧 Features:")
gr.Markdown("- Medical Q&A support\n- Easy-to-use chatbot interface\n- Privacy-focused with local data")
gr.Markdown("---")
gr.Markdown("© 2025 MEDChat AI | Finetuned by LLaMA 2 | Created for educational purposes")
with gr.Tab("Sign Up"):
with gr.Column() as signup_ui:
gr.Markdown("### 📝 Sign Up")
new_username = gr.Textbox(label="New Username")
new_password = gr.Textbox(label="New Password", type="password")
create_account_btn = gr.Button("Create Account", elem_id="create-btn")
signup_msg = gr.Markdown()
with gr.Tab("Login"):
with gr.Column(visible=True) as login_ui:
gr.Markdown("### 🔐 Login")
username = gr.Textbox(label="Username")
password = gr.Textbox(label="Password", type="password")
login_btn = gr.Button("Login", elem_id="login-btn")
login_msg = gr.Markdown()
with gr.Tab("Chat"):
with gr.Column(visible=False) as chat_ui:
gr.Markdown("## 💬 MEDChat AI")
gr.Markdown("What can I help with today?")
logout_btn = gr.Button("🚪 Logout", elem_id="logout-btn")
prompt = gr.Textbox(lines=5, placeholder="Enter your prompt...")
submit_btn = gr.Button("Submit", elem_id="submit-btn")
response = gr.Textbox(label="Response")
gr.Markdown("### Try one of these:")
examples = gr.Examples(
examples=[
["What does the immune system do?"],
["What is Epistaxis?"],
["Do our intestines contain germs?"],
["What are allergies?"],
["Should I start taking creatine?"],
["What are antibiotics?"],
["Why do I get sick?"],
["What's the difference between bacteria and viruses?"],
["Where are some places that germs hide?"],
],
inputs=prompt
)
logout_btn.click(fn=logout_user, outputs=[chat_ui, login_ui, tabs])
# Tab navigation logic
to_signup = gr.Button(visible=False)
to_login = gr.Button(visible=False)
to_signup.click(lambda: gr.update(selected=1), outputs=tabs)
to_login.click(lambda: gr.update(selected=2), outputs=tabs)
create_account_btn.click(fn=signup_user, inputs=[new_username, new_password], outputs=signup_msg)
login_btn.click(
fn=login_user,
inputs=[username, password],
outputs=[chat_ui, login_ui, login_msg, tabs]
)
submit_btn.click(fn=generate_response, inputs=prompt, outputs=response)
# Launch the interface
demo.launch(share=True) |