[Feature] Show Multi Gen Eval + Batch Prediction (#267)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#267

epdb/views.py

@@ -1,11 +1,12 @@
 import json
 import logging
 from typing import Any, Dict, List
+from datetime import datetime
 
 import nh3
 from django.conf import settings as s
 from django.contrib.auth import get_user_model
-from django.core.exceptions import BadRequest
+from django.core.exceptions import BadRequest, PermissionDenied
 from django.http import HttpResponse, HttpResponseBadRequest, HttpResponseNotAllowed, JsonResponse
 from django.shortcuts import redirect, render
 from django.urls import reverse
@@ -50,6 +51,7 @@ from .models import (
     SimpleAmbitRule,
     User,
     UserPackagePermission,
+    ExpansionSchemeChoice,
 )
 
 logger = logging.getLogger(__name__)
@@ -438,6 +440,18 @@ def predict_pathway(request):
     return render(request, "predict_pathway.html", context)
 
 
+def batch_predict_pathway(request):
+    """Top-level predict pathway view using user's default package."""
+    if request.method != "GET":
+        return HttpResponseNotAllowed(["GET"])
+
+    context = get_base_context(request)
+    context["title"] = "enviPath - Batch Predict Pathway"
+    context["meta"]["current_package"] = context["meta"]["user"].default_package
+
+    return render(request, "batch_predict_pathway.html", context)
+
+
 @package_permission_required()
 def package_predict_pathway(request, package_uuid):
     """Package-specific predict pathway view."""
@@ -1967,7 +1981,7 @@ def package_pathways(request, package_uuid):
 
         if pw_mode == "predict" or pw_mode == "incremental":
             # unlimited pred (will be handled by setting)
-            limit = -1
+            limit = None
 
             # For incremental predict first level and return
             if pw_mode == "incremental":
@@ -2877,15 +2891,25 @@ def settings(request):
             )
 
             if not PackageManager.readable(current_user, params["model"].package):
-                raise ValueError("")
+                raise PermissionDenied("You're not allowed to access this model!")
+
+            expansion_scheme = request.POST.get(
+                "model-based-prediction-setting-expansion-scheme", "BFS"
+            )
+
+            if expansion_scheme not in ExpansionSchemeChoice.values:
+                raise BadRequest(f"Unknown expansion scheme: {expansion_scheme}")
+
+            params["expansion_scheme"] = ExpansionSchemeChoice(expansion_scheme)
 
         elif tp_gen_method == "rule-based-prediction-setting":
             rule_packages = request.POST.getlist("rule-based-prediction-setting-packages")
             params["rule_packages"] = [
                 PackageManager.get_package_by_url(current_user, p) for p in rule_packages
             ]
+
         else:
-            raise ValueError("")
+            raise BadRequest("Neither Model-Based nor Rule-Based as Method selected!")
 
         created_setting = SettingManager.create_setting(
             current_user,
@@ -2963,6 +2987,59 @@ def jobs(request):
             )
 
             return redirect(f"{s.SERVER_URL}/jobs/{res.task_id}")
+
+        elif job_name == "batch-predict":
+            substrates = request.POST.get("substrates")
+            prediction_setting_url = request.POST.get("prediction-setting")
+            num_tps = request.POST.get("num-tps")
+
+            if substrates is None or substrates.strip() == "":
+                raise BadRequest("No substrates provided.")
+
+            pred_data = []
+            for pair in substrates.split("\n"):
+                parts = pair.split(",")
+
+                try:
+                    smiles = FormatConverter.standardize(parts[0])
+                except ValueError:
+                    raise BadRequest(f"Couldn't standardize SMILES {parts[0]}!")
+
+                # name is optional
+                name = parts[1] if len(parts) > 1 else None
+                pred_data.append([smiles, name])
+
+            max_tps = 50
+            if num_tps is not None and num_tps.strip() != "":
+                try:
+                    num_tps = int(num_tps)
+                    max_tps = max(min(num_tps, 50), 1)
+                except ValueError:
+                    raise BadRequest(f"Parameter for num-tps {num_tps} is not a valid integer.")
+
+            batch_predict_setting = SettingManager.get_setting_by_url(
+                current_user, prediction_setting_url
+            )
+
+            target_package = PackageManager.create_package(
+                current_user,
+                f"Autogenerated Package for Batch Prediction {datetime.now()}",
+                "This Package was generated automatically for the batch prediction task.",
+            )
+
+            from .tasks import dispatch, batch_predict
+
+            res = dispatch(
+                current_user,
+                batch_predict,
+                pred_data,
+                batch_predict_setting.pk,
+                target_package.pk,
+                num_tps=max_tps,
+            )
+
+            return redirect(f"{s.SERVER_URL}/jobs/{res.task_id}")
+
         else:
             raise BadRequest(f"Job {job_name} is not supported!")
     else:
@@ -2982,6 +3059,21 @@ def job(request, job_uuid):
         # No op if status is already in a terminal state
         job.check_for_update()
 
+        if request.GET.get("download", False) == "true":
+            if not job.is_result_downloadable():
+                raise BadRequest("Result is not downloadable!")
+
+            if job.job_name == "batch_predict":
+                filename = f"{job.job_name.replace(' ', '_')}_{job.task_id}.csv"
+            else:
+                raise BadRequest("Result is not downloadable!")
+
+            res_str = job.task_result
+            response = HttpResponse(res_str, content_type="text/csv")
+            response["Content-Disposition"] = f'attachment; filename="{filename}"'
+
+            return response
+
         context["object_type"] = "joblog"
         context["breadcrumbs"] = [
             {"Home": s.SERVER_URL},