added textModel files to github repo

.gitignore

@@ -1,207 +0,0 @@
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Dockerfile

@@ -0,0 +1,28 @@
+# Use official Python image
+FROM python:3.9-slim
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the textModel folder contents into /app
+COPY --chown=user ./textModel/ . 
+
+# Expose the port your Flask app will run on
+EXPOSE 7860
+
+# Command to run Flask app in Hugging Face Spaces
+ENV FLASK_APP=app.py
+ENV FLASK_RUN_HOST=0.0.0.0
+ENV FLASK_RUN_PORT=7860
+
+CMD ["flask", "run"]
+

README.md

@@ -1,9 +0,0 @@
----
-title: Moodify TextModel
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
-sdk: docker
-pinned: false
----
-# moodify_textModel

textModel/README.md

@@ -0,0 +1,39 @@
+# Text Emotion Detection API
+
+This is a Flask-based API that uses a pre-trained transformer model to detect emotions from text.
+
+## How to Run
+
+### 1. Prerequisites
+
+Make sure you have Python 3 and `pip` installed on your system.
+
+### 2. Create a Virtual Environment (Recommended)
+
+It's a good practice to create a virtual environment to manage project dependencies.
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+
+### 3. Install Dependencies
+
+Install the required Python packages using `pip`:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Run the Application
+
+Once the dependencies are installed, you can run the Flask application:
+
+```bash
+#first run the fix_nlkt.py file
+python fix_nltk.py
+#once this is succesfull run app.py now you can run /client code in seperate terminal with npm run dev and go to emotionQuestionnare
+
+python app.py
+```
+

textModel/app.py

@@ -0,0 +1,540 @@
+import logging
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from transformers import pipeline
+import torch
+from pymongo import MongoClient
+from pymongo.errors import ConnectionFailure
+import random
+import certifi
+from textblob import TextBlob
+
+# --- Set up logging ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Database Connection ---
+MONGO_URI = "mongodb+srv://soniyavitkar2712:soniya_27@cluster0.slai2ew.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
+client = None
+db = None
+songs_collection = None
+
+try:
+    logger.info("Attempting to connect to MongoDB Atlas...")
+    # Use certifi to provide the SSL certificate
+    ca = certifi.where()
+    client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=5000, tlsCAFile=ca)
+    # The ismaster command is cheap and does not require auth.
+    client.admin.command('ismaster')
+    db = client["moodify_db"]
+    songs_collection = db["songs_by_emotion"]
+    logger.info(f"Successfully connected to MongoDB. Using database: '{db.name}' and collection: '{songs_collection.name}'")
+except ConnectionFailure as e:
+    logger.error(f"MongoDB connection failed. Please check your MONGO_URI and network access. Error: {e}")
+    # Exit if we can't connect to the DB
+    exit()
+except Exception as e:
+    logger.error(f"An unexpected error occurred during DB initialization: {e}")
+    exit()
+
+
+app = Flask(__name__)
+CORS(app)
+
+# --- Model & Configuration ---
+emotion_classifier = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+EMOTION_MAP = {
+    'joy': 'happy',
+    'sadness': 'sad',
+    'anger': 'angry',
+    'surprise': 'surprised',
+    'neutral': 'neutral',
+}
+
+def initialize_model():
+    """Initializes the pre-trained emotion classification model."""
+    global emotion_classifier
+    try:
+        model_name = "j-hartmann/emotion-english-distilroberta-base"
+        logger.info(f"Loading model: {model_name} on device: {device}")
+        
+        emotion_classifier = pipeline(
+            "text-classification",
+            model=model_name,
+            tokenizer=model_name,
+            device=0 if device == "cuda" else -1,
+            top_k=None,
+            max_length=512,
+            truncation=True
+        )
+        logger.info("Model loaded successfully!")
+        return True
+    except Exception as e:
+        logger.error(f"Fatal error loading model: {e}")
+        emotion_classifier = None
+        return False
+
+def combine_responses(responses):
+    """Combine multiple text inputs into one."""
+    if not responses:
+        return ""
+    valid_responses = [resp.strip() for resp in responses if resp and resp.strip()]
+    combined_text = " . ".join(valid_responses)
+    words = combined_text.split()
+    if len(words) > 400:
+        combined_text = " ".join(words[:400])
+    return combined_text
+
+def correct_spelling(text):
+    """Corrects spelling mistakes in the input text using TextBlob."""
+    if not text:
+        return ""
+    try:
+        # Create a TextBlob object and call the correct() method
+        corrected_blob = TextBlob(text).correct()
+        return str(corrected_blob)
+    except Exception as e:
+        logger.error(f"Error during spelling correction: {e}")
+        # Fallback to original text if correction fails
+        return text
+
+def fetch_songs_by_emotion(emotion, limit=20):
+    """Fetch songs from MongoDB based on emotion with enhanced logging."""
+    try:
+        query_filter = {"emotion": emotion}
+        logger.info(f"Executing MongoDB find with filter: {query_filter}")
+        
+        songs = list(songs_collection.find(query_filter, {"_id": 0}).limit(limit))
+        
+        if not songs:
+            logger.warning(f"Query returned 0 songs for filter: {query_filter}")
+            case_insensitive_filter = {"emotion": {"$regex": f"^{emotion}$", "$options": "i"}}
+            case_insensitive_count = songs_collection.count_documents(case_insensitive_filter)
+            if case_insensitive_count > 0:
+                logger.warning(f"Hint: Found {case_insensitive_count} songs with case-insensitive match. Check for capitalization issues (e.g., 'Happy' vs 'happy').")
+            return []
+
+        logger.info(f"Query successfully found {len(songs)} songs for emotion: '{emotion}'")
+        random.shuffle(songs)
+        return songs
+    except Exception as e:
+        logger.error(f"Error during MongoDB query for emotion '{emotion}': {e}")
+        return []
+
+def process_emotion_predictions(text):
+    """Analyzes text, filters for relevant emotions, maps them, and returns sorted results."""
+    raw_predictions = emotion_classifier(text)
+    
+    mapped_predictions = []
+    for pred in raw_predictions[0]:
+        raw_emotion = pred['label'].lower()
+        if raw_emotion in EMOTION_MAP:
+            mapped_predictions.append({
+                'emotion': EMOTION_MAP[raw_emotion],
+                'confidence': round(pred['score'], 4)
+            })
+            
+    # --- MODIFICATION START ---
+    # If no emotions from the EMOTION_MAP are found, fallback to 'neutral'.
+    if not mapped_predictions:
+        logger.warning(f"No mapped emotions found in predictions. Falling back to 'neutral'.")
+        return [{'emotion': 'neutral', 'confidence': 1.0}]
+    # --- END MODIFICATION ---
+
+    mapped_predictions.sort(key=lambda x: x['confidence'], reverse=True)
+    return mapped_predictions
+
+
+@app.route('/health', methods=['GET'])
+def health_check():
+    """Health check endpoint for server, model, and database status."""
+    try:
+        client.admin.command('ping')
+        db_status = "connected"
+        db_info = f"Using database '{db.name}' with {songs_collection.count_documents({})} songs."
+    except Exception as e:
+        db_status = "disconnected"
+        db_info = str(e)
+    
+    return jsonify({
+        'status': 'healthy',
+        'model_status': "loaded" if emotion_classifier else "not loaded",
+        'device': device,
+        'database_status': db_status,
+        'database_info': db_info
+    })
+
+@app.route('/predict', methods=['POST'])
+def predict_emotion():
+    """Predict emotion, return all relevant emotion scores, and provide songs."""
+    if not emotion_classifier:
+        return jsonify({'error': 'Model is not available. Please try again later.'}), 503
+
+    try:
+        data = request.get_json()
+        if not data or 'responses' not in data:
+            return jsonify({'error': 'Invalid input. Provide "responses" field in JSON.'}), 400
+
+        original_text = combine_responses(data.get('responses', []))
+        if not original_text.strip():
+            return jsonify({'error': 'Input text is empty after processing.'}), 400
+
+        logger.info(f"Original text received: '{original_text}'")
+        corrected_text = correct_spelling(original_text)
+        logger.info(f"Text after spell correction: '{corrected_text}'")
+
+        final_emotions = process_emotion_predictions(corrected_text)
+        
+        # This check is now effectively redundant due to the fallback, but safe to keep.
+        if not final_emotions:
+            return jsonify({'error': 'Could not determine a relevant emotion from the provided text.'}), 400
+
+        primary_emotion_obj = final_emotions[0]
+        primary_emotion = primary_emotion_obj['emotion']
+        
+        songs = fetch_songs_by_emotion(primary_emotion)
+
+        return jsonify({
+            'primary_emotion': primary_emotion,
+            'confidence': primary_emotion_obj['confidence'],
+            'all_emotions': final_emotions,
+            'original_text_preview': original_text[:150] + ('...' if len(original_text) > 150 else ''),
+            'corrected_text_preview': corrected_text[:150] + ('...' if len(corrected_text) > 150 else ''),
+            'songs': songs,
+            'songs_count': len(songs)
+        })
+
+    except Exception as e:
+        logger.error(f"Error in prediction endpoint: {e}")
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+@app.route('/text_emotion/predict', methods=['POST'])
+def predict_emotion_text():
+    if not emotion_classifier:
+        return jsonify({'error': 'Model is not available. Please try again later.'}), 503
+    try:
+        data = request.get_json()
+        if not data or 'responses' not in data:
+            return jsonify({'error': 'Invalid input. Provide "responses" field in JSON.'}), 400
+        
+        original_text = combine_responses(data.get('responses', []))
+        if not original_text.strip():
+            return jsonify({'error': 'Input text is empty after processing.'}), 400
+
+        logger.info(f"Original text received: '{original_text}'")
+        corrected_text = correct_spelling(original_text)
+        logger.info(f"Text after spell correction: '{corrected_text}'")
+
+        final_emotions = process_emotion_predictions(corrected_text)
+
+        # This check is now effectively redundant due to the fallback, but safe to keep.
+        if not final_emotions:
+            return jsonify({'error': 'Could not determine a relevant emotion from the provided text.'}), 400
+        primary_emotion_obj = final_emotions[0]
+
+        return jsonify({
+            'primary_emotion': primary_emotion_obj['emotion'],
+            'confidence': primary_emotion_obj['confidence'],
+            'all_emotions': final_emotions,
+            'original_text_preview': original_text[:150] + ('...' if len(original_text) > 150 else ''),
+            'corrected_text_preview': corrected_text[:150] + ('...' if len(corrected_text) > 150 else '')
+        })
+    except Exception as e:
+        logger.error(f"Error in text_emotion prediction: {e}")
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+@app.route('/songs/<emotion>', methods=['GET'])
+def get_songs_by_emotion(emotion):
+    limit = request.args.get('limit', 20, type=int)
+    songs = fetch_songs_by_emotion(emotion.lower(), limit)
+    return jsonify({'emotion': emotion, 'songs': songs, 'count': len(songs)})
+
+@app.route('/songs/all', methods=['GET'])
+def get_all_emotions():
+    try:
+        emotions = sorted(songs_collection.distinct("emotion"))
+        emotion_counts = {emo: songs_collection.count_documents({"emotion": emo}) for emo in emotions}
+        return jsonify({'emotions': emotions, 'emotion_counts': emotion_counts})
+    except Exception as e:
+        logger.error(f"Error fetching all emotions: {e}")
+        return jsonify({'error': f'Failed to fetch emotions: {str(e)}'}), 500
+
+if __name__ == '__main__':
+    logger.info("Starting Emotion Detection API...")
+    if emotion_classifier or initialize_model():
+        app.run(debug=True, host='0.0.0.0', port=5001)
+    else:
+        logger.error("Could not start the server because the model failed to initialize.")
+
+# import logging
+# from flask import Flask, request, jsonify
+# from flask_cors import CORS
+# from transformers import pipeline
+# import torch
+# from pymongo import MongoClient
+# from pymongo.errors import ConnectionFailure
+# import random
+# import certifi
+# from textblob import TextBlob # --- NEW ---
+
+# # --- Set up logging ---
+# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# logger = logging.getLogger(__name__)
+
+# # --- Database Connection ---
+# MONGO_URI = "mongodb+srv://soniyavitkar2712:soniya_27@cluster0.slai2ew.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
+# client = None
+# db = None
+# songs_collection = None
+
+# try:
+#     logger.info("Attempting to connect to MongoDB Atlas...")
+#     # Use certifi to provide the SSL certificate
+#     ca = certifi.where()
+#     client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=5000, tlsCAFile=ca)
+#     # The ismaster command is cheap and does not require auth.
+#     client.admin.command('ismaster')
+#     db = client["moodify_db"]
+#     songs_collection = db["songs_by_emotion"]
+#     logger.info(f"Successfully connected to MongoDB. Using database: '{db.name}' and collection: '{songs_collection.name}'")
+# except ConnectionFailure as e:
+#     logger.error(f"MongoDB connection failed. Please check your MONGO_URI and network access. Error: {e}")
+#     # Exit if we can't connect to the DB
+#     exit()
+# except Exception as e:
+#     logger.error(f"An unexpected error occurred during DB initialization: {e}")
+#     exit()
+
+
+# app = Flask(__name__)
+# CORS(app)
+
+# # --- Model & Configuration ---
+# emotion_classifier = None
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# EMOTION_MAP = {
+#     'joy': 'happy',
+#     'sadness': 'sad',
+#     'anger': 'angry',
+#     'surprise': 'surprised',
+#     'neutral': 'neutral',
+# }
+
+# def initialize_model():
+#     """Initializes the pre-trained emotion classification model."""
+#     global emotion_classifier
+#     try:
+#         model_name = "j-hartmann/emotion-english-distilroberta-base"
+#         logger.info(f"Loading model: {model_name} on device: {device}")
+        
+#         emotion_classifier = pipeline(
+#             "text-classification",
+#             model=model_name,
+#             tokenizer=model_name,
+#             device=0 if device == "cuda" else -1,
+#             top_k=None,
+#             max_length=512,
+#             truncation=True
+#         )
+#         logger.info("Model loaded successfully!")
+#         return True
+#     except Exception as e:
+#         logger.error(f"Fatal error loading model: {e}")
+#         emotion_classifier = None
+#         return False
+
+# def combine_responses(responses):
+#     """Combine multiple text inputs into one."""
+#     if not responses:
+#         return ""
+#     valid_responses = [resp.strip() for resp in responses if resp and resp.strip()]
+#     combined_text = " . ".join(valid_responses)
+#     words = combined_text.split()
+#     if len(words) > 400:
+#         combined_text = " ".join(words[:400])
+#     return combined_text
+
+# # --- NEW: Function to correct spelling ---
+# def correct_spelling(text):
+#     """Corrects spelling mistakes in the input text using TextBlob."""
+#     if not text:
+#         return ""
+#     try:
+#         # Create a TextBlob object and call the correct() method
+#         corrected_blob = TextBlob(text).correct()
+#         return str(corrected_blob)
+#     except Exception as e:
+#         logger.error(f"Error during spelling correction: {e}")
+#         # Fallback to original text if correction fails
+#         return text
+
+# def fetch_songs_by_emotion(emotion, limit=20):
+#     """Fetch songs from MongoDB based on emotion with enhanced logging."""
+#     try:
+#         query_filter = {"emotion": emotion}
+#         logger.info(f"Executing MongoDB find with filter: {query_filter}")
+        
+#         songs = list(songs_collection.find(query_filter, {"_id": 0}).limit(limit))
+        
+#         if not songs:
+#             logger.warning(f"Query returned 0 songs for filter: {query_filter}")
+#             case_insensitive_filter = {"emotion": {"$regex": f"^{emotion}$", "$options": "i"}}
+#             case_insensitive_count = songs_collection.count_documents(case_insensitive_filter)
+#             if case_insensitive_count > 0:
+#                 logger.warning(f"Hint: Found {case_insensitive_count} songs with case-insensitive match. Check for capitalization issues (e.g., 'Happy' vs 'happy').")
+#             return []
+
+#         logger.info(f"Query successfully found {len(songs)} songs for emotion: '{emotion}'")
+#         random.shuffle(songs)
+#         return songs
+#     except Exception as e:
+#         logger.error(f"Error during MongoDB query for emotion '{emotion}': {e}")
+#         return []
+
+# def process_emotion_predictions(text):
+#     """Analyzes text, filters for relevant emotions, maps them, and returns sorted results."""
+#     raw_predictions = emotion_classifier(text)
+    
+#     mapped_predictions = []
+#     for pred in raw_predictions[0]:
+#         raw_emotion = pred['label'].lower()
+#         if raw_emotion in EMOTION_MAP:
+#             mapped_predictions.append({
+#                 'emotion': EMOTION_MAP[raw_emotion],
+#                 'confidence': round(pred['score'], 4)
+#             })
+            
+#     if not mapped_predictions:
+#         return None
+
+#     mapped_predictions.sort(key=lambda x: x['confidence'], reverse=True)
+#     return mapped_predictions
+
+
+# @app.route('/health', methods=['GET'])
+# def health_check():
+#     """Health check endpoint for server, model, and database status."""
+#     try:
+#         client.admin.command('ping')
+#         db_status = "connected"
+#         db_info = f"Using database '{db.name}' with {songs_collection.count_documents({})} songs."
+#     except Exception as e:
+#         db_status = "disconnected"
+#         db_info = str(e)
+    
+#     return jsonify({
+#         'status': 'healthy',
+#         'model_status': "loaded" if emotion_classifier else "not loaded",
+#         'device': device,
+#         'database_status': db_status,
+#         'database_info': db_info
+#     })
+
+# @app.route('/predict', methods=['POST'])
+# def predict_emotion():
+#     """Predict emotion, return all relevant emotion scores, and provide songs."""
+#     if not emotion_classifier:
+#         return jsonify({'error': 'Model is not available. Please try again later.'}), 503
+
+#     try:
+#         data = request.get_json()
+#         if not data or 'responses' not in data:
+#             return jsonify({'error': 'Invalid input. Provide "responses" field in JSON.'}), 400
+
+#         original_text = combine_responses(data.get('responses', []))
+#         if not original_text.strip():
+#             return jsonify({'error': 'Input text is empty after processing.'}), 400
+
+#         # --- MODIFIED: Add spelling correction step ---
+#         logger.info(f"Original text received: '{original_text}'")
+#         corrected_text = correct_spelling(original_text)
+#         logger.info(f"Text after spell correction: '{corrected_text}'")
+
+#         final_emotions = process_emotion_predictions(corrected_text)
+#         # --- END MODIFICATION ---
+        
+#         if not final_emotions:
+#             return jsonify({'error': 'Could not determine a relevant emotion from the provided text.'}), 400
+
+#         primary_emotion_obj = final_emotions[0]
+#         primary_emotion = primary_emotion_obj['emotion']
+        
+#         songs = fetch_songs_by_emotion(primary_emotion)
+
+#         # --- MODIFIED: Add corrected text to the response for clarity ---
+#         return jsonify({
+#             'primary_emotion': primary_emotion,
+#             'confidence': primary_emotion_obj['confidence'],
+#             'all_emotions': final_emotions,
+#             'original_text_preview': original_text[:150] + ('...' if len(original_text) > 150 else ''),
+#             'corrected_text_preview': corrected_text[:150] + ('...' if len(corrected_text) > 150 else ''),
+#             'songs': songs,
+#             'songs_count': len(songs)
+#         })
+
+#     except Exception as e:
+#         logger.error(f"Error in prediction endpoint: {e}")
+#         return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+# @app.route('/text_emotion/predict', methods=['POST'])
+# def predict_emotion_text():
+#     if not emotion_classifier:
+#         return jsonify({'error': 'Model is not available. Please try again later.'}), 503
+#     try:
+#         data = request.get_json()
+#         if not data or 'responses' not in data:
+#             return jsonify({'error': 'Invalid input. Provide "responses" field in JSON.'}), 400
+        
+#         original_text = combine_responses(data.get('responses', []))
+#         if not original_text.strip():
+#             return jsonify({'error': 'Input text is empty after processing.'}), 400
+
+#         # --- MODIFIED: Add spelling correction step ---
+#         logger.info(f"Original text received: '{original_text}'")
+#         corrected_text = correct_spelling(original_text)
+#         logger.info(f"Text after spell correction: '{corrected_text}'")
+
+#         final_emotions = process_emotion_predictions(corrected_text)
+#         # --- END MODIFICATION ---
+
+#         if not final_emotions:
+#             return jsonify({'error': 'Could not determine a relevant emotion from the provided text.'}), 400
+#         primary_emotion_obj = final_emotions[0]
+
+#         # --- MODIFIED: Add corrected text to the response for clarity ---
+#         return jsonify({
+#             'primary_emotion': primary_emotion_obj['emotion'],
+#             'confidence': primary_emotion_obj['confidence'],
+#             'all_emotions': final_emotions,
+#             'original_text_preview': original_text[:150] + ('...' if len(original_text) > 150 else ''),
+#             'corrected_text_preview': corrected_text[:150] + ('...' if len(corrected_text) > 150 else '')
+#         })
+#     except Exception as e:
+#         logger.error(f"Error in text_emotion prediction: {e}")
+#         return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+# @app.route('/songs/<emotion>', methods=['GET'])
+# def get_songs_by_emotion(emotion):
+#     limit = request.args.get('limit', 20, type=int)
+#     songs = fetch_songs_by_emotion(emotion.lower(), limit)
+#     return jsonify({'emotion': emotion, 'songs': songs, 'count': len(songs)})
+
+# @app.route('/songs/all', methods=['GET'])
+# def get_all_emotions():
+#     try:
+#         emotions = sorted(songs_collection.distinct("emotion"))
+#         emotion_counts = {emo: songs_collection.count_documents({"emotion": emo}) for emo in emotions}
+#         return jsonify({'emotions': emotions, 'emotion_counts': emotion_counts})
+#     except Exception as e:
+#         logger.error(f"Error fetching all emotions: {e}")
+#         return jsonify({'error': f'Failed to fetch emotions: {str(e)}'}), 500
+
+# if __name__ == '__main__':
+#     logger.info("Starting Emotion Detection API...")
+#     if emotion_classifier or initialize_model():
+#         app.run(debug=True, host='0.0.0.0', port=5001)
+#     else:
+#         logger.error("Could not start the server because the model failed to initialize.")

textModel/checkMongo.py

@@ -0,0 +1,27 @@
+from pymongo import MongoClient
+import certifi
+import os
+
+# MongoDB connection string
+MONGO_URI = os.getenv("MONGO_URI")
+if not MONGO_URI:
+    raise ValueError("MONGO_URI environment variable is not set")
+
+# Connect using certifi's CA bundle
+client = MongoClient(MONGO_URI, tlsCAFile=certifi.where())
+
+# Access database and collection
+db = client["moodify_db"]
+collection = db["songs_by_emotion"]
+
+# Function to fetch songs by emotion
+def get_songs_by_emotion(emotion):
+    results = collection.find({"emotion": emotion})
+    return list(results)
+
+# Main
+if __name__ == "__main__":
+    emotion = input("Enter an emotion (e.g. happy, sad, angry): ")
+    songs = get_songs_by_emotion(emotion)
+    for song in songs:
+        print(song)

textModel/fix_nltk.py

@@ -0,0 +1,57 @@
+# Run this script to download required NLTK data
+import nltk
+import ssl
+
+# Handle SSL certificate issues if they occur
+try:
+    _create_unverified_https_context = ssl._create_unverified_context
+except AttributeError:
+    pass
+else:
+    ssl._create_default_https_context = _create_unverified_https_context
+
+# Download required NLTK data
+print("Downloading NLTK data...")
+
+try:
+    # Download the newer punkt_tab tokenizer
+    nltk.download('punkt_tab')
+    print("✓ punkt_tab downloaded successfully")
+except:
+    print("Failed to download punkt_tab, trying punkt...")
+    try:
+        nltk.download('punkt')
+        print("✓ punkt downloaded successfully")
+    except:
+        print("Failed to download punkt tokenizer")
+
+try:
+    # Download stopwords
+    nltk.download('stopwords')
+    print("✓ stopwords downloaded successfully")
+except:
+    print("Failed to download stopwords")
+
+print("NLTK setup complete!")
+
+# Test the downloads
+try:
+    from nltk.tokenize import sent_tokenize
+    from nltk.corpus import stopwords
+    
+    test_text = "Hello world. This is a test."
+    sentences = sent_tokenize(test_text)
+    stop_words = set(stopwords.words('english'))
+    
+    print(f"\nTesting tokenization:")
+    print(f"Input: {test_text}")
+    print(f"Sentences: {sentences}")
+    print(f"Number of English stopwords: {len(stop_words)}")
+    print("\n✓ All NLTK components working correctly!")
+    
+except Exception as e:
+    print(f"\n❌ Error testing NLTK components: {e}")
+    print("You may need to run the downloads manually in Python:")
+    print(">>> import nltk")
+    print(">>> nltk.download('punkt_tab')")
+    print(">>> nltk.download('stopwords')")

textModel/requirements.txt

@@ -0,0 +1,14 @@
+flask==2.3.3
+flask-cors==4.0.0
+torch>=1.9.0
+transformers>=4.20.0
+nltk>=3.8
+numpy>=1.21.0
+scipy>=1.7.0
+scikit-learn>=1.0.0
+requests>=2.25.0
+tqdm>=4.62.0
+cloudinary
+gunicorn
+flask-pymongo
+os