FactChecker files(final_deploy)

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_training/visualizations/fake_news_wordcloud.png filter=lfs diff=lfs merge=lfs -text
+model_training/visualizations/feature_importance.png filter=lfs diff=lfs merge=lfs -text
+model_training/visualizations/real_news_wordcloud.png filter=lfs diff=lfs merge=lfs -text
+model_training/visualizations/roc_curves.png filter=lfs diff=lfs merge=lfs -text
+model_training/visualizations/text_length_distribution.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,28 @@
+# Hugging Face Docker config.
+FROM python:3.9
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Download NLTK resources(location:all users)
+RUN python -c "import nltk; nltk.download('punkt', download_dir='/usr/local/share/nltk_data'); nltk.download('stopwords', download_dir='/usr/local/share/nltk_data'); nltk.download('wordnet', download_dir='/usr/local/share/nltk_data')"
+
+# Create a user
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+RUN python -c "import nltk; nltk.download('punkt', download_dir='/usr/local/share/nltk_data'); nltk.download('stopwords', download_dir='/usr/local/share/nltk_data'); nltk.download('wordnet', download_dir='/usr/local/share/nltk_data')"
+RUN python -c "import nltk; nltk.download('punkt_tab', download_dir='/usr/local/share/nltk_data')"
+ENV NLTK_DATA="/usr/local/share/nltk_data"
+##
+
+# Copy application files
+COPY --chown=user ./app.py .
+COPY --chown=user ./models ./models
+COPY --chown=user ./build ./build
+
+# Command to run Flask app with gunicorn
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

README.md

@@ -1,12 +1,12 @@
----
-title: FactChecker
-emoji: 🏃
-colorFrom: red
-colorTo: yellow
-sdk: docker
-pinned: false
-license: mit
-short_description: 'FactChecker: Fake News Detector'
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: FactChecker
+emoji: 📚
+colorFrom: pink
+colorTo: red
+sdk: docker
+pinned: false
+license: mit
+short_description: 'FactChecker: Fake News Detector'
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,170 @@
+from flask import Flask, request, jsonify, send_from_directory
+import pickle
+import torch
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from transformers import DistilBertTokenizer, DistilBertModel
+import torch.nn as nn
+import os
+import numpy
+
+# Download NLTK stuff
+nltk.data.path.append('/usr/local/share/nltk_data')
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+nltk.download('wordnet')
+nltk.download('punkt')
+
+app = Flask(__name__, static_folder='build', static_url_path='')
+
+# Define DistilBERT model class
+class DistilBERTClassifier(nn.Module):
+    def __init__(self, dropout_rate=0.2):
+        super(DistilBERTClassifier, self).__init__()
+        self.distilbert = DistilBertModel.from_pretrained('distilbert-base-uncased')
+        self.dropout = nn.Dropout(dropout_rate)
+        self.classifier = nn.Linear(768, 2)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.distilbert(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.last_hidden_state[:, 0]
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+
+# Clean text function
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r'http\S+|www\S+|https\S+', '', text)
+    text = re.sub(r'<.*?>', '', text)
+    text = re.sub(r'[^\w\s]', '', text)
+    text = re.sub(r'\d+', '', text)
+    
+    tokens = nltk.word_tokenize(text)
+    
+    stop_words = set(stopwords.words('english'))
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
+    
+    cleaned_text = ' '.join(tokens)
+    return cleaned_text
+
+# Load models
+def load_models():
+    # Set device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # TF-IDF vectorizer
+    with open('models/tfidf_vectorizer.pkl', 'rb') as f:
+        tfidf_vectorizer = pickle.load(f)
+        
+    # Logistic Regression
+    with open('models/lr_model.pkl', 'rb') as f:
+        lr_model = pickle.load(f)
+        
+    # random Forest
+    with open('models/rf_model.pkl', 'rb') as f:
+        rf_model = pickle.load(f)
+        
+    # load DistilBERT
+    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+    distilbert_model = DistilBERTClassifier()
+    distilbert_model.load_state_dict(torch.load('models/distilbert_model.pt', map_location=device))
+    distilbert_model.to(device)
+    distilbert_model.eval()
+    
+    return tfidf_vectorizer, lr_model, rf_model, distilbert_model, tokenizer, device
+
+# Load models at startup
+tfidf_vectorizer, lr_model, rf_model, distilbert_model, tokenizer, device = load_models()
+
+@app.route('/')
+def serve():
+    return send_from_directory(app.static_folder, 'index.html')
+
+@app.route('/api/analyze', methods=['POST'])
+def analyze():
+    data = request.get_json()
+    
+    if not data or 'text' not in data or 'model' not in data:
+        return jsonify({'error': 'Missing required fields'}), 400
+    news_text = data['text']
+    model_option = data['model']
+    
+    if not news_text:
+        return jsonify({'error': 'Text cannot be empty'}), 400
+    
+    # Clean text
+    cleaned_text = clean_text(news_text)
+    
+    results = {}
+    
+    # Using Logistic Regression
+    if model_option in ["lr", "all"]:
+        text_tfidf = tfidf_vectorizer.transform([cleaned_text])
+        lr_pred = lr_model.predict(text_tfidf)[0]
+        lr_prob = lr_model.predict_proba(text_tfidf)[0]
+        results["Logistic Regression"] = {
+            "prediction": "Real" if lr_pred == 1 else "Fake",
+            "fake_prob": float(lr_prob[0]),
+            "real_prob": float(lr_prob[1])
+        }
+    
+    # Using Random Forest
+    if model_option in ["rf", "all"]:
+        text_tfidf = tfidf_vectorizer.transform([cleaned_text])
+        rf_pred = rf_model.predict(text_tfidf)[0]
+        rf_prob = rf_model.predict_proba(text_tfidf)[0]
+        results["Random Forest"] = {
+            "prediction": "Real" if rf_pred == 1 else "Fake",
+            "fake_prob": float(rf_prob[0]),
+            "real_prob": float(rf_prob[1])
+        }
+    
+    # Using DistilBERT
+    if model_option in ["distilbert", "all"]:
+        encoding = tokenizer(
+            cleaned_text,
+            truncation=True,
+            padding='max_length',
+            max_length=128,
+            return_tensors='pt'
+        )
+        
+        with torch.no_grad():
+            input_ids = encoding['input_ids'].to(device)
+            attention_mask = encoding['attention_mask'].to(device)
+            outputs = distilbert_model(input_ids=input_ids, attention_mask=attention_mask)
+            print("Raw model output:", outputs.cpu().numpy())
+            probs = torch.softmax(outputs, dim=1).cpu().numpy()[0]
+            print("After softmax:", probs)
+
+            print(f"Text: {cleaned_text[:50]}...")
+            print(f"Probabilities: Real={probs[0]:.4f}, Fake={probs[1]:.4f}")
+            
+        distilbert_pred = 1 if probs[1] > probs[0] else 0
+        results["DistilBERT"] = {
+            "prediction": "Real" if distilbert_pred == 1 else "Fake",
+            "fake_prob": float(probs[0]),
+            "real_prob": float(probs[1])
+        }
+    
+    # Calculate overall results for "all models" option
+    if model_option == "all":
+        real_votes = sum(1 for model, result in results.items() if result["prediction"] == "Real")
+        fake_votes = len(results) - real_votes
+        overall_verdict = "Real" if real_votes >= fake_votes else "Fake"
+        results["Overall"] = {
+            "prediction": overall_verdict,
+            "real_votes": real_votes,
+            "fake_votes": fake_votes,
+            "total_models": len(results)
+        }
+    
+    return jsonify({'results': results})
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 7860))
+    app.run(host='0.0.0.0', port=port)

build/asset-manifest.json

@@ -0,0 +1,13 @@
+{
+  "files": {
+    "main.css": "/static/css/main.b34208b9.css",
+    "main.js": "/static/js/main.c63a58f2.js",
+    "index.html": "/index.html",
+    "main.b34208b9.css.map": "/static/css/main.b34208b9.css.map",
+    "main.c63a58f2.js.map": "/static/js/main.c63a58f2.js.map"
+  },
+  "entrypoints": [
+    "static/css/main.b34208b9.css",
+    "static/js/main.c63a58f2.js"
+  ]
+}

build/index.html

@@ -0,0 +1 @@
+<!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/logo.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Web site created using create-react-app"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>FactChecker</title><script defer="defer" src="/static/js/main.c63a58f2.js"></script><link href="/static/css/main.b34208b9.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>

build/manifest.json

@@ -0,0 +1,25 @@
+{
+  "short_name": "React App",
+  "name": "Create React App Sample",
+  "icons": [
+    {
+      "src": "favicon.ico",
+      "sizes": "64x64 32x32 24x24 16x16",
+      "type": "image/x-icon"
+    },
+    {
+      "src": "logo192.png",
+      "type": "image/png",
+      "sizes": "192x192"
+    },
+    {
+      "src": "logo512.png",
+      "type": "image/png",
+      "sizes": "512x512"
+    }
+  ],
+  "start_url": ".",
+  "display": "standalone",
+  "theme_color": "#000000",
+  "background_color": "#ffffff"
+}

build/robots.txt

@@ -0,0 +1,3 @@
+# https://www.robotstxt.org/robotstxt.html
+User-agent: *
+Disallow:

build/static/css/main.b34208b9.css

@@ -0,0 +1,2 @@
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+/*# sourceMappingURL=main.b34208b9.css.map*/

build/static/css/main.b34208b9.css.map

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background-color: #ffefef;\r\n  color: #e74c3c;\r\n  font-size: 14px;\r\n}\r\n\r\n/* Results styling */\r\n.results {\r\n  padding: 20px;\r\n}\r\n\r\n.results h2 {\r\n  font-size: 18px;\r\n  margin-bottom: 20px;\r\n  font-weight: 600;\r\n  color: #333;\r\n}\r\n\r\n.result-item {\r\n  padding: 16px;\r\n  border-radius: 8px;\r\n  background-color: #f9f9f9;\r\n  margin-bottom: 16px;\r\n}\r\n\r\n.result-item.real {\r\n  border-left: 4px solid #2ecc71;\r\n}\r\n\r\n.result-item.fake {\r\n  border-left: 4px solid #e74c3c;\r\n}\r\n\r\n.result-header {\r\n  display: flex;\r\n  justify-content: space-between;\r\n  align-items: center;\r\n  margin-bottom: 12px;\r\n}\r\n\r\n.result-header h3 {\r\n  font-size: 16px;\r\n  font-weight: 600;\r\n  color: #333;\r\n}\r\n\r\n.prediction-badge {\r\n  padding: 6px 12px;\r\n  border-radius: 20px;\r\n  font-size: 14px;\r\n  font-weight: 600;\r\n}\r\n\r\n.real .prediction-badge {\r\n  background-color: #e8f7f0;\r\n  color: #2ecc71;\r\n}\r\n\r\n.fake .prediction-badge {\r\n  background-color: #fdeae8;\r\n  color: #e74c3c;\r\n}\r\n\r\n.confidence {\r\n  margin-top: 12px;\r\n}\r\n\r\n.confidence-bar {\r\n  height: 12px;\r\n  background-color: #f0f0f0;\r\n  border-radius: 6px;\r\n  overflow: hidden;\r\n  margin-bottom: 8px;\r\n}\r\n\r\n.confidence-fill {\r\n  height: 100%;\r\n  background-color: #4F8DF8;\r\n  border-radius: 6px;\r\n  position: relative;\r\n}\r\n\r\n.confidence-fill span {\r\n  position: absolute;\r\n  right: 8px;\r\n  top: -3px;\r\n  font-size: 12px;\r\n  font-weight: 600;\r\n  color: white;\r\n}\r\n\r\n.confidence p {\r\n  font-size: 12px;\r\n  color: #888;\r\n  text-align: right;\r\n}\r\n\r\n.back-button {\r\n  margin-top: 16px;\r\n  background-color: #e9ecef;\r\n  color: #495057;\r\n}\r\n\r\n.back-button:hover {\r\n  background-color: #dee2e6;\r\n}\r\n\r\n.logo-image {\r\n  width: 50px; \r\n  height: 50px;\r\n  object-fit: contain;\r\n}\r\n\r\n.icon {\r\n  display: flex;\r\n  align-items: center;\r\n}"],"names":[],"sourceRoot":""}

build/static/js/main.c63a58f2.js.LICENSE.txt

@@ -0,0 +1,49 @@
+/**
+ * @license React
+ * react-dom-client.production.js
+ *
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * react-dom.production.js
+ *
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * react-jsx-runtime.production.js
+ *
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * react.production.js
+ *
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+/**
+ * @license React
+ * scheduler.production.js
+ *
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */

models/distilbert_model.pt

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+oid sha256:0181662839f9ed56480b01882d344bb0f3ea133fe154a184562e089651dc413f
+size 265499424

models/lr_model.pkl

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+oid sha256:abbb63f0866d81b83fbddf21308879f1f4d475f24b337050c0a9e31f9bccbc8b
+size 80784

models/rf_model.pkl

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+size 53206112

models/tfidf_vectorizer.pkl

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+oid sha256:7eba5171ee839fc50cf4d5eed7bf3e5e1d6a7822cd6cbe00b0e368fa77ad070f
+size 372373

requirements.txt

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+flask==2.0.1
+werkzeug==2.0.3
+torch==2.6.0
+transformers==4.49.0
+scikit-learn==1.6.1
+nltk==3.9.1
+regex==2023.6.3
+numpy==2.0.2
+gunicorn==20.1.0
+matplotlib==3.7.2