Update loggıng

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, render_template, request, redirect, url_for
+from flask import Flask, render_template, request
 from joblib import load
 import pandas as pd
 import re
@@ -9,156 +9,100 @@ import numpy as np
 from huggingface_hub import hf_hub_download
 import torch
 import os
+import logging
 
+# Ensure proper display for debugging
 pd.set_option('display.max_colwidth', 1000)
 
-
 # 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
 
-def load_pipeline_from_hub(filename):
-    cache_dir = "/tmp/hf_cache"
-    os.environ["HF_HUB_CACHE"] = cache_dir  # optional but informative
-
-    repo_id = 'hw01558/nlp-coursework-pipelines'
-    local_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
-    return load(local_path)
-    
-    #repo_id = 'hw01558/nlp-coursework-pipelines'
-    #local_path = hf_hub_download(repo_id=repo_id, filename=filename)
-    #return load(local_path)
+# Flask app setup
+app = Flask(__name__)
 
+# Logging setup
+LOG_DIR = "/tmp/logs"  # Use a universally writable directory
+LOG_FILE = os.path.join(LOG_DIR, "usage_log.jsonl")
+os.makedirs(LOG_DIR, exist_ok=True)
+logging.basicConfig(
+    filename=LOG_FILE,
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s'
+)
+
+# Define pipelines
 PIPELINES = [
-    {
-         'id': 8,
-         'name': 'Embedded using BioWordVec',
-        'filename': "pipeline_ex3_s4.joblib"
-    },
-    {
-        'id': 1,
-        'name': 'Baseline',
-        'filename': "pipeline_ex1_s1.joblib"
-    },
-    {
-        'id': 2,
-        'name': 'Trained on a FeedForward NN',
-        'filename': "pipeline_ex1_s2.joblib"
-    },
-    {
-        'id': 3,
-        'name': 'Trained on a CRF',
-        'filename': "pipeline_ex1_s3.joblib"
-    },
-    {
-        'id': 4,
-        'name': 'Trained on a small dataset',
-        'filename': "pipeline_ex2_s3.joblib"
-    },
-    {
-        'id': 5,
-        'name': 'Trained on a large dataset',
-        'filename': "pipeline_ex2_s2.joblib"
-    },
-    {
-        'id': 6,
-        'name': 'Embedded using TFIDF',
-        'filename': "pipeline_ex3_s2.joblib"
-    },
-    {
-        'id': 7,
-        'name': 'Embedded using GloVe',
-        'filename': "pipeline_ex3_s3.joblib"
-    },
-    
-    
+    {'id': 8, 'name': 'Embedded using BioWordVec', 'filename': "pipeline_ex3_s4.joblib"},
+    {'id': 1, 'name': 'Baseline', 'filename': "pipeline_ex1_s1.joblib"},
+    {'id': 2, 'name': 'Trained on a FeedForward NN', 'filename': "pipeline_ex1_s2.joblib"},
+    {'id': 3, 'name': 'Trained on a CRF', 'filename': "pipeline_ex1_s3.joblib"},
+    {'id': 4, 'name': 'Trained on a small dataset', 'filename': "pipeline_ex2_s3.joblib"},
+    {'id': 5, 'name': 'Trained on a large dataset', 'filename': "pipeline_ex2_s2.joblib"},
+    {'id': 6, 'name': 'Embedded using TFIDF', 'filename': "pipeline_ex3_s2.joblib"},
+    {'id': 7, 'name': 'Embedded using GloVe', 'filename': "pipeline_ex3_s3.joblib"},
 ]
 
 pipeline_metadata = [{'id': p['id'], 'name': p['name']} for p in PIPELINES]
 
+# Helper functions
+def load_pipeline_from_hub(filename):
+    cache_dir = "/tmp/hf_cache"
+    os.environ["HF_HUB_CACHE"] = cache_dir
+    repo_id = 'hw01558/nlp-coursework-pipelines'
+    local_path = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
+    return load(local_path)
+
 def get_pipeline_by_id(pipelines, pipeline_id):
     return next((p['filename'] for p in pipelines if p['id'] == pipeline_id), None)
 
 def get_name_by_id(pipelines, pipeline_id):
     return next((p['name'] for p in pipelines if p['id'] == pipeline_id), None)
 
-
-
 def requestResults(text, pipeline):
     labels = pipeline.predict(text)
     if isinstance(labels, np.ndarray):
         labels = labels.tolist()
     return labels[0]
 
-import os
-import logging
-
-#logging.basicConfig(
-#    level=logging.INFO,
- #   format='%(asctime)s [%(levelname)s] %(message)s',
-  #  handlers=[
-   #     logging.FileHandler("app.log",mode='w')
-    #    
-    #]
-    
-#)
-
-LOG_FILE = "./usage_log.jsonl"  # Use temporary file path for Hugging Face Spaces
-LOG_FILE = os.path.join("logs", "usage_log.jsonl")
-
 def log_interaction(user_input, model_name, predictions):
-    # https://betterstack.com/community/guides/logging/how-to-start-logging-with-python/
-    logging.basicConfig(filename=LOG_FILE, level=logging.INFO)
     log_entry = {
         "timestamp": datetime.datetime.utcnow().isoformat(),
         "model": model_name,
         "user_input": user_input,
         "predictions": predictions
     }
-
     try:
-        os.makedirs("logs", exist_ok=True)
-       # with open(LOG_FILE, "a") as log_file:
-       #     log_file.write(json.dumps(log_entry) + "\n")
-        logging.info(log_entry)
+        logging.info(json.dumps(log_entry))
         print("[INFO] Logged interaction successfully.")
     except Exception as e:
         print(f"[ERROR] Could not write log entry: {e}")
 
-
-app = Flask(__name__)
-
-
+# Routes
 @app.route('/')
 def index():
-    return render_template('index.html', pipelines= pipeline_metadata)
-
+    return render_template('index.html', pipelines=pipeline_metadata)
 
 @app.route('/', methods=['POST'])
 def get_data():
     if request.method == 'POST':
-
         text = request.form['search']
         tokens = re.findall(r"\w+|[^\w\s]", text)
-        tokens_fomatted = pd.Series([pd.Series(tokens)])
+        tokens_formatted = pd.Series([pd.Series(tokens)])
 
         pipeline_id = int(request.form['pipeline_select'])
         pipeline = load_pipeline_from_hub(get_pipeline_by_id(PIPELINES, pipeline_id))
         name = get_name_by_id(PIPELINES, pipeline_id)
-        
-        labels = requestResults(tokens_fomatted, pipeline)
+
+        labels = requestResults(tokens_formatted, pipeline)
         results = dict(zip(tokens, labels))
 
         log_interaction(text, name, results)
 
-        return render_template('index.html', results=results, name=name, pipelines= pipeline_metadata)
-
+        return render_template('index.html', results=results, name=name, pipelines=pipeline_metadata)
 
+# Run the app
 if __name__ == '__main__':
     app.run(host="0.0.0.0", port=7860)
-
-#if __name__ == '__main__':
-#app.run(host="0.0.0.0", port=7860)