Feature/service agent

.gitignore

@@ -41,3 +41,9 @@ _history
 # Institution specific config
 config/*_setting.rb
 !config/no_institution_setting.rb
+
+# Ignore TorchServe logs and config
+ml_services/logs/*
+
+# Ignore TorchServe auto-generated key file
+ml_services/key_file.json

Gemfile.lock

@@ -174,6 +174,7 @@ GEM
     faraday-net_http (3.4.0)
       net-http (>= 0.5.0)
     ffi (1.17.1-aarch64-linux-gnu)
+    ffi (1.17.1-arm64-darwin)
     ffi (1.17.1-x86_64-linux-gnu)
     fugit (1.11.1)
       et-orbi (~> 1, >= 1.2.11)
@@ -281,6 +282,8 @@ GEM
     nio4r (2.7.4)
     nokogiri (1.18.7-aarch64-linux-gnu)
       racc (~> 1.4)
+    nokogiri (1.18.7-arm64-darwin)
+      racc (~> 1.4)
     nokogiri (1.18.7-x86_64-linux-gnu)
       racc (~> 1.4)
     numerizer (0.1.1)
@@ -559,6 +562,7 @@ GEM
 
 PLATFORMS
   aarch64-linux
+  arm64-darwin-23
   x86_64-linux
 
 DEPENDENCIES

app/api/tasks_api.rb

@@ -519,4 +519,15 @@ class TasksApi < Grape::API
     true
   end
 
+  # effort prediction endpoint for task..
+  desc 'Predict effort for a task'
+  params do
+    requires :features, type: Array[Float], desc: 'Feature values'
+  end
+  post :predict_effort do
+    features = params[:features]
+    prediction = EffortPredictionService.predict(features)
+    { predicted_effort: prediction }
+  end
+
 end

app/controllers/task_downloads_controller.rb

@@ -49,4 +49,28 @@ def index
   rescue MyException => e
     render json: e.message, status: e.status
   end
+
+  # prediction effort function
+
+  protect_from_forgery with: :null_session # allow API POST without CSRF token
+  # skip_before_action :verify_authenticity_token, only: [:predict_effort]
+
+  # POST /tasks/predict_effort
+  def predict_effort
+    features = params[:features]
+
+    if features.blank?
+      render json: { error: "Features parameter is required" }, status: :bad_request
+      return
+    end
+
+    prediction_value = EffortPredictionService.predicted_effort(features)
+
+    if prediction_value
+      render json: { predicted_effort: prediction_value }
+    else
+      render json: { error: "Prediction failed" }, status: :internal_server_error
+    end
+  end
+
 end

app/services/effort_prediction_service.rb

@@ -0,0 +1,45 @@
+# app/services/effort_prediction_service.rb
+
+require 'net/http'
+require 'json'
+
+class EffortPredictionService
+  TORCHSERVE_URL = ENV.fetch("TORCHSERVE_URL", "http://effort-predictor:8080/predictions/effort-predictor")
+
+  def self.predict(features)
+    uri = URI(TORCHSERVE_URL)
+    headers = {
+      "Content-Type" => "application/json",
+      "Authorization" => "Bearer #{ENV.fetch('TORCHSERVE_INFERENCE_KEY', nil)}"
+    }
+    body = { features: features }.to_json
+
+    response = Net::HTTP.post(uri, body, headers)
+
+    if response.is_a?(Net::HTTPSuccess)
+      parsed = begin
+        JSON.parse(response.body)
+      rescue StandardError
+        response.body
+      end
+      parsed.is_a?(Hash) ? parsed["predicted_effort"] || parsed.values.first : parsed
+    else
+      Rails.logger.error("TorchServe error: #{response.code} #{response.body}")
+      nil
+    end
+  end
+
+  # Added a new helper method
+  def self.predicted_effort(features)
+    result = predict(features)
+    case result
+    when Array
+      result.first
+    when Hash
+      result["predicted_effort"] || result.values.first
+    else
+      result
+    end
+  end
+
+end

config/routes.rb

@@ -5,6 +5,7 @@
   get 'api/submission/unit/:id/task_definitions/:task_def_id/download_submissions', to: 'task_downloads#index'
   get 'api/submission/unit/:id/task_definitions/:task_def_id/student_pdfs', to: 'task_submission_pdfs#index'
   get 'api/units/:id/all_resources', to: 'lecture_resource_downloads#index'
+  post 'tasks/predict_effort', to: 'task_downloads#predict_effort'
 
   mount ApiRoot => '/'
   mount GrapeSwaggerRails::Engine => '/api/docs'

ml_services/dummy_model.py

@@ -0,0 +1,16 @@
+import torch
+import torch.nn as nn
+
+class EffortPredictor(nn.Module):
+    def __init__(self, input_dim):
+        super(EffortPredictor, self).__init__()
+        self.fc = nn.Linear(input_dim, 1)
+
+    def forward(self, x):
+        return self.fc(x)
+
+# Create a dummy model with 10 input features
+model = EffortPredictor(input_dim=10)
+
+# Save its state dict as effort_model.pth
+torch.save(model.state_dict(), "effort_model.pth")

ml_services/handlers/effort_regression_handler.py

@@ -0,0 +1,31 @@
+
+import json
+import torch
+from ts.torch_handler.base_handler import BaseHandler
+
+class EffortRegressionHandler(BaseHandler):
+    def postprocess(self, data):
+        if isinstance(data, torch.Tensor):
+            return {"predicted_effort": float(data.item())}
+        return {"predicted_effort": data}
+
+    def handle(self, data, context):
+        try:
+            # Extract request body
+            body = data[0].get("body")
+
+            features = body["features"]   # no json.loads
+
+            # Convert to tensor
+            tensor = torch.tensor(features).float().unsqueeze(0)
+
+            # Run model
+
+            output = max(0.0, self.model(tensor).item()) # to have positive output
+
+
+            # Return JSON response
+            return [json.dumps({"predicted_effort": output})]
+
+        except Exception as e:
+            return [json.dumps({"error": str(e)})]

ml_services/key_file.json

@@ -0,0 +1,13 @@
+{
+  "management": {
+    "key": "ZT_SKghL",
+    "expiration time": "2026-04-13T15:16:21.380749386Z"
+  },
+  "inference": {
+    "key": "AJVoXRTf",
+    "expiration time": "2026-04-13T15:16:21.380728803Z"
+  },
+  "API": {
+    "key": "hIvWRijq"
+  }
+}

ml_services/models/effort_model.py

@@ -0,0 +1,16 @@
+import torch
+import torch.nn as nn
+
+class EffortPredictor(nn.Module):
+    def __init__(self, input_dim=10): # initialise with a default value
+        super(EffortPredictor, self).__init__()
+        self.fc = nn.Linear(input_dim, 1)
+
+    def forward(self, x):
+        return self.fc(x)
+
+def get_model():
+    model = EffortPredictor(input_dim=10)  # adjust to your features
+    model.load_state_dict(torch.load("effort_model.pth"))
+    model.eval()
+    return model

ml_services/scripts/build_mar.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+torch-model-archiver \
+  --model-name effort-predictor \
+  --version 1.0 \
+  --model-file models/effort_model.py \
+  --serialized-file effort_model.pth \
+  --handler handlers/effort_regression_handler.py \
+  --export-path model_store \
+  --force

texlive.Dockerfile

@@ -31,7 +31,8 @@ RUN apt-get update && \
 ENV PATH=$PATH:/opt/texlive/bin/x86_64-linux:/opt/texlive/bin/aarch64-linux
 
 # Install required TeX Live packages for lualatex compilation
-RUN tlmgr install \
+RUN tlmgr update --self && tlmgr update --all && tlmgr install \
+#RUN tlmgr install \
   catchfile \
   csvsimple \
   environ \
@@ -53,7 +54,7 @@ RUN tlmgr install \
   paralist \
   pdfcol \
   pdflscape \
-  pdfmanagement-testphase \
+  #pdfmanagement-testphase \
   pdfpages \
   tagpdf \
   tcolorbox \