Update app.py
Browse files
app.py
CHANGED
@@ -2,7 +2,7 @@
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import os
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import shutil
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import glob
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import base64
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import streamlit as st
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import pandas as pd
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import torch
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@@ -77,13 +77,15 @@ class ModelBuilder:
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self.tokenizer = None
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self.sft_data = None
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def load_model(self, model_path: str):
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"""Load a model from a path"""
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with st.spinner("Loading model... β³"):
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self.model = AutoModelForCausalLM.from_pretrained(model_path)
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self.tokenizer = AutoTokenizer.from_pretrained(model_path)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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st.success("Model loaded! β
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return self
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@@ -156,7 +158,9 @@ selected_model = st.sidebar.selectbox("Select Saved Model", ["None"] + model_dir
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if selected_model != "None" and st.sidebar.button("Load Model π"):
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if 'builder' not in st.session_state:
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st.session_state['builder'] = ModelBuilder()
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st.session_state['model_loaded'] = True
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st.rerun()
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@@ -176,7 +180,7 @@ with tab1:
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if st.button("Download Model β¬οΈ"):
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config = ModelConfig(name=model_name, base_model=base_model, size="small", domain=domain)
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builder = ModelBuilder()
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builder.load_model(base_model)
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builder.save_model(config.model_path)
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st.session_state['builder'] = builder
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st.session_state['model_loaded'] = True
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@@ -210,7 +214,12 @@ with tab2:
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with open(csv_path, "wb") as f:
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f.write(uploaded_csv.read())
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new_model_name = f"{st.session_state['builder'].config.name}-sft-{int(time.time())}"
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new_config = ModelConfig(
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st.session_state['builder'].config = new_config
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with st.status("Fine-tuning model... β³", expanded=True) as status:
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st.session_state['builder'].fine_tune_sft(csv_path)
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import os
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import shutil
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import glob
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import base64
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import streamlit as st
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import pandas as pd
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import torch
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self.tokenizer = None
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self.sft_data = None
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def load_model(self, model_path: str, config: Optional[ModelConfig] = None):
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"""Load a model from a path with an optional config"""
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with st.spinner("Loading model... β³"):
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self.model = AutoModelForCausalLM.from_pretrained(model_path)
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self.tokenizer = AutoTokenizer.from_pretrained(model_path)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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if config:
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self.config = config
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st.success("Model loaded! β
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return self
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if selected_model != "None" and st.sidebar.button("Load Model π"):
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if 'builder' not in st.session_state:
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st.session_state['builder'] = ModelBuilder()
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# Create a config for the loaded model if none exists
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config = ModelConfig(name=os.path.basename(selected_model), base_model="unknown", size="small", domain="general")
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st.session_state['builder'].load_model(selected_model, config)
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st.session_state['model_loaded'] = True
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st.rerun()
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if st.button("Download Model β¬οΈ"):
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config = ModelConfig(name=model_name, base_model=base_model, size="small", domain=domain)
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builder = ModelBuilder()
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builder.load_model(base_model, config) # Pass config here
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builder.save_model(config.model_path)
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st.session_state['builder'] = builder
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st.session_state['model_loaded'] = True
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with open(csv_path, "wb") as f:
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f.write(uploaded_csv.read())
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new_model_name = f"{st.session_state['builder'].config.name}-sft-{int(time.time())}"
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new_config = ModelConfig(
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name=new_model_name,
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base_model=st.session_state['builder'].config.base_model,
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size="small",
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domain=st.session_state['builder'].config.domain
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)
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st.session_state['builder'].config = new_config
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with st.status("Fine-tuning model... β³", expanded=True) as status:
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st.session_state['builder'].fine_tune_sft(csv_path)
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