| 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) |
|
|
