Upload folder using huggingface_hub
Browse files- Dockerfile +1 -1
- app.py +6 -6
- requirements.txt +2 -3
Dockerfile
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@@ -4,7 +4,7 @@ FROM python:3.12-slim
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# Set the working directory inside the container to /app
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WORKDIR /app
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# Copy all files from the current directory on the host to the container's /app directory
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COPY . .
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# Install Python dependencies listed in requirements.txt
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# Set the working directory inside the container to /app
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WORKDIR /app
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# Copy all files from the current directory on the host to the container's /app # directory
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COPY . .
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# Install Python dependencies listed in requirements.txt
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app.py
CHANGED
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@@ -2,13 +2,13 @@ import streamlit as st
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import pandas as pd
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import joblib
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import os
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import dill
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import logging
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from huggingface_hub import login,hf_hub_download
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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cache_dir = "/tmp/hf_cache"
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os.environ["HF_HOME"] = cache_dir
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os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir
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@@ -55,7 +55,7 @@ class PredictorTourism:
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try:
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logger.info("Loading best model")
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model_path = hf_hub_download(
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repo_id = self.repoID,filename = f'Model_Dump_JOBLIB/
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repo_type = 'model')
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threshold_path = hf_hub_download(
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repo_id = self.repoID, filename=f'Model_Dump_JOBLIB/best_threshold.txt',
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@@ -64,9 +64,9 @@ class PredictorTourism:
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logger.info(f"Model path: {model_path}")
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logger.info(f"Threshold path: {threshold_path}")
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-
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with open(model_path, 'rb') as f:
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-
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with open(threshold_path,'r') as f:
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self.best_threshold = float(f.read())
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st.success("Model and threshold loaded successfully")
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import pandas as pd
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import joblib
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import os
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import logging
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from huggingface_hub import login,hf_hub_download
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+
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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os.environ["STREAMLIT_CONFIG_DIR"] = "/tmp/.streamlit"
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cache_dir = "/tmp/hf_cache"
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os.environ["HF_HOME"] = cache_dir
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os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir
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try:
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logger.info("Loading best model")
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model_path = hf_hub_download(
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repo_id = self.repoID,filename = f'Model_Dump_JOBLIB/BestModel_GradientBoostingClassifier.joblib',
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repo_type = 'model')
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threshold_path = hf_hub_download(
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repo_id = self.repoID, filename=f'Model_Dump_JOBLIB/best_threshold.txt',
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logger.info(f"Model path: {model_path}")
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logger.info(f"Threshold path: {threshold_path}")
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self.model = joblib.load(model_path)
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# with open(model_path, 'rb') as f:
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# self.model = joblib.load(f)
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with open(threshold_path,'r') as f:
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self.best_threshold = float(f.read())
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st.success("Model and threshold loaded successfully")
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requirements.txt
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@@ -1,8 +1,7 @@
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pandas
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numpy
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scikit-learn
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joblib
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streamlit
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huggingface_hub
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-
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dill
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pandas
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numpy
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scikit-learn==1.6.1
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joblib
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streamlit
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huggingface_hub
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setuptools
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