Sync from GitHub (preserve manual model files)

StreamlitApp/utils/predict.py

@@ -1,12 +1,8 @@
-import os
 import pathlib
-import requests
 import numpy as np
 import torch
 import streamlit as st
 from torch import nn
-from typing import Optional
-import shutil
 
 # Model Definition
 class FastMLP(nn.Module):
@@ -24,21 +20,6 @@ class FastMLP(nn.Module):
     def forward(self, x):
         return self.layers(x)
 
-# Utility: download file from URL to local path (streaming)
-def _download_file(url: str, dest_path: str):
-    dest = pathlib.Path(dest_path)
-    dest.parent.mkdir(parents=True, exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        r.raise_for_status()
-        with open(dest, 'wb') as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                if chunk:
-                    f.write(chunk)
-
-def _get_env(key: str) -> Optional[str]:
-    v = os.environ.get(key)
-    return v if v else None
-
 # Model Loader
 @st.cache_resource
 def load_model():
@@ -53,60 +34,13 @@ def load_model():
     ]
     model_path = next((p for p in candidates if p.exists()), candidates[0])
 
-    # If the model file doesn't exist, try to download it from a configured URL
     if not model_path.exists():
-
-        model_url = _get_env("MODEL_URL")
-
-        if model_url:
-            try:
-                _download_file(model_url, str(model_path))
-            except Exception as e:
-                st.error(f"Failed to download model from MODEL_URL: {e}")
-                raise
-        else:
-            # Fall back to Hugging Face Hub model repo download.
-            # Configure these in HF Space secrets/vars, or locally in env:
-            # - MODEL_REPO_ID (e.g. "m0ksh/peptideai-models")
-            # - MODEL_FILENAME (default: "ampMLModel.pt")
-            model_repo_id = _get_env("MODEL_REPO_ID")
-            model_filename = _get_env("MODEL_FILENAME") or "ampMLModel.pt"
-
-            if not model_repo_id:
-                raise FileNotFoundError(
-                    "Model file './models/ampMLModel.pt' not found.\n"
-                    "Set one of:\n"
-                    "- MODEL_URL (direct download URL), or\n"
-                    "- MODEL_REPO_ID (Hugging Face model repo id) and optional MODEL_FILENAME.\n"
-                    "\n"
-                    "Debug (env vars detected): "
-                    f"MODEL_URL={'set' if _get_env('MODEL_URL') else 'missing'}, "
-                    f"MODEL_REPO_ID={'set' if _get_env('MODEL_REPO_ID') else 'missing'}, "
-                    f"MODEL_FILENAME={'set' if _get_env('MODEL_FILENAME') else 'missing'}\n"
-                )
-
-            try:
-                from huggingface_hub import hf_hub_download
-            except Exception as e:
-                raise RuntimeError(
-                    "Missing dependency 'huggingface_hub'. Add it to requirements.txt.\n"
-                    f"Import error: {e}"
-                ) from e
-
-            token = _get_env("HF_TOKEN") or _get_env("HUGGINGFACE_TOKEN")
-            downloaded_path = hf_hub_download(
-                repo_id=model_repo_id,
-                filename=model_filename,
-                token=token,
-            )
-            model_path.parent.mkdir(parents=True, exist_ok=True)
-            shutil.copyfile(downloaded_path, model_path)
-
-        if not model_path.exists():
-            raise FileNotFoundError(
-                f"Model download did not produce file at: {model_path}\n"
-                "Check MODEL_URL or MODEL_REPO_ID/MODEL_FILENAME configuration."
-            )
+        raise FileNotFoundError(
+            "Model file 'ampMLModel.pt' not found in any of:\n"
+            f"- {repo_root / 'MLModels' / 'ampMLModel.pt'}\n"
+            f"- {repo_root / 'models' / 'ampMLModel.pt'}\n"
+            f"- {streamlitapp_dir / 'models' / 'ampMLModel.pt'}\n"
+        )
 
     # Build model and load weights
     model = FastMLP(input_dim=1024)

requirements.txt

@@ -5,5 +5,4 @@ torch
 scikit-learn
 matplotlib
 plotly
-requests
-huggingface_hub
+requests