Add new pipelines and adjust labels

Added new pipelines in PIPELINES
Made sure onto torch goes onto cpu (Unsure if this is needed)
Changed requestResults as new way of dealing with labels in training of models
Tried to add logs back in

app.py

@@ -2,48 +2,64 @@ from flask import Flask, render_template, request, redirect, url_for
 from joblib import load
 import pandas as pd
 import re
-from customFunctions import *
+from customFunctions2 import *
 import json
 import datetime
+import numpy as np
 
 pd.set_option('display.max_colwidth', 1000)
 
+import torch
+
+# Patch torch.load to always load on CPU
+original_torch_load = torch.load
+def cpu_load(*args, **kwargs):
+    return original_torch_load(*args, map_location=torch.device('cpu'), **kwargs)
+
+torch.load = cpu_load
+
+
 PIPELINES = [
     {
         'id': 1,
         'name': 'Baseline',
-        'pipeline': load("pipeline_ex1_s1.joblib")
+        'pipeline': load("pipelines/pipeline_ex1_s1.joblib")
     },
     {
         'id': 2,
         'name': 'Trained on a FeedForward NN',
-        'pipeline': load("pipeline_ex1_s2.joblib")
+        'pipeline': load("pipelines/pipeline_ex1_s2.joblib")
     },
     {
         'id': 3,
         'name': 'Trained on a CRF',
-        'pipeline': load("pipeline_ex1_s3.joblib")
+        'pipeline': load("pipelines/pipeline_ex1_s3.joblib")
+    },
+    {
+        'id': 4,
+        'name': 'Trained on a small dataset',
+        'pipeline': load("pipelines/pipeline_ex2_s3.joblib")
+    },
+    {
+        'id': 5,
+        'name': 'Trained on a large dataset',
+        'pipeline': load("pipelines/pipeline_ex2_s2.joblib")
+    },
+    {
+        'id': 6,
+        'name': 'Embedded using TFIDF',
+        'pipeline': load("pipelines/pipeline_ex3_s2.joblib")
+    },
+    {
+        'id': 7,
+        'name': 'Embedded using GloVe',
+        'pipeline': load("pipelines/pipeline_ex3_s3.joblib")
+    },
+    {
+         'id': 8,
+         'name': 'Embedded using Bio2Vec',
+         'pipeline': load("pipelines/pipeline_ex3_s4.joblib")
     },
-    #{
-     #   'id': 4,
-      #  'name': 'Trained on a small dataset',
-       # 'pipeline': load("pipeline_ex2_s1.joblib")
-    #},
-    #{
-     #   'id': 5,
-      #  'name': 'Trained on a large dataset',
-       # 'pipeline': load("pipeline_ex2_s2.joblib")
-    #},
-    #{
-     #   'id': 6,
-      #  'name': 'Embedded using TFIDF',
-       # 'pipeline': load("pipeline_ex3_s1.joblib")
-    #},
-    #{
-     #   'id': 7,
-      #  'name': 'Embedded using ?',
-       # 'pipeline': load("pipeline_ex3_s2.joblib")
-    #},
     
 ]
 
@@ -59,30 +75,29 @@ def get_name_by_id(pipelines, pipeline_id):
 
 def requestResults(text, pipeline):
     labels = pipeline.predict(text)
-    print(labels.ndim)
-    if labels.ndim != 1:
-        flattened_predictions = []
-        for sentence in labels:
-            for tag in sentence:
-                flattened_predictions.append(tag)
-        labels = flattened_predictions
-    print(labels)
-    labels = [int(label) for label in labels]
-    tag_encoder = LabelEncoder()
-    tag_encoder.fit(['B-AC', 'O', 'B-LF', 'I-LF'])
-    decoded_labels = tag_encoder.inverse_transform(labels)
-    return decoded_labels
-
-LOG_FILE = "usage_log.jsonl"  # Each line is a JSON object
+    if isinstance(labels, np.ndarray):
+        labels = labels.tolist()
+    return labels[0]
 
-def log_interaction(user_input, model_name, predictions):
-    print("====== Interaction Log ======")
-    print("Timestamp:", datetime.datetime.utcnow().isoformat())
-    print("Model:", model_name)
-    print("Input:", user_input)
-    print("Predictions:", predictions)
-    print("=============================")
+import os
+
+LOG_FILE = "/tmp/usage_log.jsonl"  # Use temporary file path for Hugging Face Spaces
 
+def log_interaction(user_input, model_name, predictions):
+    log_entry = {
+        "timestamp": datetime.datetime.utcnow().isoformat(),
+        "model": model_name,
+        "user_input": user_input,
+        "predictions": predictions
+    }
+
+    try:
+        os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True)  # Ensure the directory exists
+        with open(LOG_FILE, "a") as log_file:
+            log_file.write(json.dumps(log_entry) + "\n")
+    except Exception as e:
+        print(f"Error writing to log: {e}")
+        # You could also return a response with the error, or raise an error to stop the process
 
 
 app = Flask(__name__)
@@ -108,10 +123,7 @@ def get_data():
         labels = requestResults(tokens_fomatted, pipeline)
         results = dict(zip(tokens, labels))
 
-        print(f"[INFO] Model: {name}")
-        print(f"[INFO] Input: {text}")
-        print(f"[INFO] Output: {results}")
-
+        log_interaction(text, name, results)
 
         return render_template('index.html', results=results, name=name, pipelines= pipeline_metadata)
 
@@ -119,5 +131,3 @@ def get_data():
 if __name__ == '__main__':
     app.run(host="0.0.0.0", port=7860)
 
-#if __name__ == '__main__':
-#app.run(host="0.0.0.0", port=7860)