update

Dockerfile

@@ -1,11 +1,19 @@
 FROM python:3.9-slim
 
+# Set working directory inside the container
 WORKDIR /app
 
+# Set the TRANSFORMERS_CACHE environment variable to a writable directory
+ENV TRANSFORMERS_CACHE=/app/cache
+
+# Copy the contents of your local directory to the working directory in the container
 COPY . .
 
+# Install dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
+# Expose port 8080 for your app
 EXPOSE 8080
 
+# Run the Flask app
 CMD ["python", "app.py"]

app.py

@@ -1,7 +1,15 @@
+import os
 from pathlib import Path
 from flask import Flask, render_template, request, jsonify
-from transformers import  BertTokenizer, BertForSequenceClassification, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import warnings
+
+# Suppress FutureWarnings
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Set the TRANSFORMERS_CACHE to a writable directory
+os.environ["TRANSFORMERS_CACHE"] = "./cache"  # Modify this path if needed
 
 app = Flask(__name__)
 
@@ -12,7 +20,6 @@ BERT_TOKENIZER = 'bert-base-uncased'
 ROBERTA_TOKENIZER = 'jcblaise/roberta-tagalog-base'
 ELECTRA_TOKENIZER = 'google/electra-base-discriminator'
 
-
 LABELS = ["fake", "real"]
 
 class Classifier:
@@ -54,19 +61,15 @@ class Classifier:
         }
         return result
 
-
-
 @app.route('/')
 def home():
     return render_template('index.html')
 
 @app.route('/detect', methods=['POST'])
 def detect():
-    
     try:
         data = request.get_json()
         news_text = data.get('text')
-        
         model_chosen = data.get('model')
 
         print(model_chosen)
@@ -77,50 +80,47 @@ def detect():
                 'message': 'No text provided'
             }), 400
         
-        switch={
-            'nonaug-bert':'bert-nonaug',
-            'aug-bert':'bert-aug',
-            'nonaug-tagbert':'tagbert-nonaug',
-            'aug-tagbert':'tagbert-aug',
-            'nonaug-electra':'electra-nonaug',
-            'aug-electra':'electra-aug'
+        switch = {
+            'nonaug-bert': 'bert-nonaug',
+            'aug-bert': 'bert-aug',
+            'nonaug-tagbert': 'tagbert-nonaug',
+            'aug-tagbert': 'tagbert-aug',
+            'nonaug-electra': 'electra-nonaug',
+            'aug-electra': 'electra-aug'
         }
 
         model_p = switch.get(model_chosen)
 
-        print("model",model_p)
-
-        MODEL_PATH = Path("D:\\Aplil\\skibidi-thesis\\webapp", model_p)
+        print("model", model_p)
 
+        # Adjusting the model path to point to the correct folder inside 'webapp'
+        MODEL_PATH = Path("huggingface", "webapp", model_p)  # Corrected model path to webapp folder
 
         print(MODEL_PATH)
 
         tokenizer = model_chosen.split("-")[1]
-
         tokenizer_chosen = {
-            'bert':BERT_TOKENIZER,
-            'tagbert':ROBERTA_TOKENIZER,
-            'electra':ELECTRA_TOKENIZER
+            'bert': BERT_TOKENIZER,
+            'tagbert': ROBERTA_TOKENIZER,
+            'electra': ELECTRA_TOKENIZER
         }
 
         print(tokenizer)
 
-        classifier = Classifier(MODEL_PATH,DEVICE,tokenizer_chosen.get(tokenizer))
+        classifier = Classifier(MODEL_PATH, DEVICE, tokenizer_chosen.get(tokenizer))
 
         result = classifier.predict(news_text)
         print(result['confidence_scores'])
-        
 
         if result['predicted_class'] == "fake":
             out = "News Needs Further Validation"
         else:
             out = "News is Real"
 
-
         return jsonify({
             'status': 'success',
             'prediction': out,
-            'confidence':result['confidence_scores']
+            'confidence': result['confidence_scores']
         })
 
     except Exception as e: