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62f1804 | 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 | import os, pickle, pandas as pd, streamlit as st
from huggingface_hub import hf_hub_download
import logging
logging.basicConfig(level=logging.INFO)
MODEL_LOCAL_PATH = "models/best_model.pkl"
MODEL_REPO = os.environ.get("MODEL_REPO", "username/model-name")
HF_TOKEN = os.environ.get("HF_TOKEN", None)
REPO_TYPE = os.environ.get("HF_REPO_TYPE", "model")
def ensure_model():
if os.path.exists(MODEL_LOCAL_PATH):
return MODEL_LOCAL_PATH
os.makedirs("models", exist_ok=True)
p = hf_hub_download(repo_id=MODEL_REPO, filename="best_model.pkl", repo_type=REPO_TYPE, token=HF_TOKEN)
with open(p, "rb") as r, open(MODEL_LOCAL_PATH, "wb") as w:
w.write(r.read())
return MODEL_LOCAL_PATH
@st.cache_resource
def load_model():
path = ensure_model()
with open(path, "rb") as f:
model = pickle.load(f)
return model
st.title("Model Inference (Streamlit)")
st.write("Upload a CSV file or paste JSON/CSV rows to get predictions.")
uploaded = st.file_uploader("Upload CSV", type=["csv"])
if uploaded is not None:
df = pd.read_csv(uploaded)
st.write("Input preview:", df.head())
if st.button("Predict"):
model = load_model()
preds = model.predict(df)
df["prediction"] = preds
st.write(df)
text_input = st.text_area("Paste CSV text or JSON list of dicts", height=150)
if st.button("Predict from text") and text_input.strip():
try:
df2 = pd.read_csv(pd.io.common.StringIO(text_input))
except Exception:
import json
df2 = pd.DataFrame(json.loads(text_input))
st.write("Parsed input:", df2.head())
model = load_model()
preds = model.predict(df2)
df2["prediction"] = preds
st.write(df2)
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