fix

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_params filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,79 @@
+import streamlit as st
+from annotated_text import annotated_text
+
+import os
+import torch.nn as nn
+import torch.nn.functional as F
+import torch 
+import torch.optim as optim
+from transformers import DistilBertModel
+from transformers import AutoTokenizer
+import lightning.pytorch as pl
+class Classifier(pl.LightningModule):
+
+    def __init__(self):
+        super().__init__()
+        self.ln1 = torch.nn.Linear(512*768, 3)
+        # self.ln2 = torch.nn.Linear(1000, 3 )
+        self.criterion = nn.CrossEntropyLoss()
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        with torch.no_grad():
+            x = get_bert()(input_ids = x[:,:512], attention_mask = x[:,512:]).last_hidden_state.reshape(-1, 512*768)
+            x = (x/torch.linalg.norm(x,2, 1)).reshape(-1,512*768)
+        x = self.ln1(x)
+        # x = self.ln2(x)
+        loss = self.criterion(x, y)
+        self.log("my_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        return loss
+    def configure_optimizers(self):
+        optimizer = optim.Adam(self.parameters(), lr=1e-3)
+        return optimizer
+    def preprocess(self, x):
+        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", use_fast=True)
+        return tokenizer(x, padding='max_length',  return_tensors="pt")
+    def forward(self, x):
+        print("here!", self.ln1.type)
+        with torch.no_grad():
+            x = get_bert()(**x).last_hidden_state.reshape(-1, 512*768)
+            x = (x/torch.linalg.norm(x,2, 1)).reshape(-1,512*768)
+            x = self.ln1(x)
+            # x = self.ln2(x)
+            return x
+
+@st.cache
+def get_bert():
+    return DistilBertModel.from_pretrained("distilbert-base-uncased")
+
+@st.cache
+def get_classifier():
+    os.system('gdown 1GxhHvg3lwlGpA7So06v3l43U8pSASy9L')
+    return Classifier.load_from_checkpoint(f"{os.getcwd()}/model_params")
+
+def get_annotated_text(text):
+    model = get_classifier()
+    text = text.split(".")
+    l = []
+
+    for i in text:
+        if i == '' or i == " ":
+            continue
+        c = model(model.preprocess([i])).argmax()
+        print("class : ", c)
+        if c == 0:
+            l.append((i, "Leadership"))
+        if c == 1:
+            l.append((i, "Diversity")) 
+        if c == 2:
+            l.append((i, "Integrity"))
+        l.append(".")
+    return tuple(l)
+
+st.title("Code of Conduct Classifier")
+
+input_text = st.text_area("enter code of conduct text" )
+
+st.title("annotated text")
+print(input_text)
+
+annotated_text(*get_annotated_text(input_text))

requirements.txt

@@ -0,0 +1,4 @@
+st-annotated-text==3.0.0
+torch==2.0.0.dev20230125
+lightning==2.0.0
+gdown==4.6.4