Update app.py

app.py

@@ -1,4 +1,41 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+import torch
+import transformers
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import os
+import torch
+import torch.nn as nn
+import bitsandbytes as bnb
+from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
+
+from datasets import Dataset
+import pandas as pd
+import transformers
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model 
+
+import time
+import logging
+
+peft_model_id = "foobar8675/bloom-7b1-lora-tagger"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-7b1", return_dict=True, load_in_8bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1")
+
+# # Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+text = st.text_area('enter text')
+
+if text:
+
+    start_time = time.time()
+    batch = tokenizer("“FINDINGS: There is bilateral narrowing the femoral tibial joint mild on the right moderate on the left. There is scalloping of the lateral tibial plateau of the left knee with small subchondral cysts and sclerosis as well as lateral degenerative osteophyte formation. There is mild narrowing of the right patellofemoral joint and moderate narrowing of the left. Degenerative osteophytes arises from the posterior inferior margin of the left patella. There is a large well corticated 2.5 cm calcification adjacent to the anterior tibial tubercle of the proximal right tibia. There is no joint effusion. The soft tissues are normal. Mild scattered vascular calcifications are noted involving the distal left superficial femoral and popliteal arteries.” ->: ", return_tensors='pt')
+    output_tokens = model.generate(**batch, max_new_tokens=25)
+
+    out = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+    st.json(out)
+    st.json(f"Elapsed time: {time.time() - start_time}s")