created a Mental health bot with ML and NLP

Mental Health chatbot/Mental_health_bot.py

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+import pandas as pd
+import numpy as np
+import json
+import nltk
+from nltk.stem import WordNetLemmatizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+import os
+
+# Download necessary NLTK data
+nltk.download('punkt')
+nltk.download('wordnet')
+
+# Initialize lemmatizer
+lemmatizer = WordNetLemmatizer()
+
+# Load datasets
+try:
+    print("Files in current directory:", os.listdir())
+    csv_files = [f for f in os.listdir() if f.endswith('.csv') and 'Combined' in f and 'Data' in f]
+    if csv_files:
+        df = pd.read_csv(csv_files[0])
+        print(f"Loaded file: {csv_files[0]}")
+        print("DataFrame columns:", df.columns.tolist())
+        print("DataFrame shape:", df.shape)
+        print(df.head())
+    else:
+        raise FileNotFoundError("No matching CSV file found")
+except FileNotFoundError as e:
+    print(f"Error: {str(e)}")
+    print("Ensure 'Combined_Data.csv' is in the same directory.")
+    print("Current working directory:", os.getcwd())
+    exit(1)
+
+# Load intents.json
+try:
+    with open('intents.json', 'r') as f:
+        intents = json.load(f)
+except FileNotFoundError as e:
+    print(f"Error: {str(e)}. Ensure 'intents.json' is available.")
+    exit(1)
+
+# Preprocess text data
+def preprocess_text(text):
+    tokens = nltk.word_tokenize(str(text).lower())
+    return ' '.join([lemmatizer.lemmatize(token) for token in tokens])
+
+# Determine the correct column name for text data
+text_column = 'statement' if 'statement' in df.columns else df.columns[0]
+
+df['processed_text'] = df[text_column].apply(preprocess_text)
+
+# Create TF-IDF vectorizer
+vectorizer = TfidfVectorizer()
+tfidf_matrix = vectorizer.fit_transform(df['processed_text'])
+
+# Prepare data for model training
+X, y = [], []
+
+for intent in intents['intents']:
+    for pattern in intent['patterns']:
+        X.append(preprocess_text(pattern))
+        y.append(intent['tag'])
+
+X = vectorizer.transform(X).toarray()
+y = pd.get_dummies(y).values
+
+# Ensure X and y have the same shape
+assert X.shape[0] == y.shape[0], f"Shape mismatch: X={X.shape}, y={y.shape}"
+
+# Build the model
+model = Sequential([
+    Dense(128, input_shape=(X.shape[1],), activation='relu'),
+    Dropout(0.5),
+    Dense(64, activation='relu'),
+    Dropout(0.5),
+    Dense(y.shape[1], activation='softmax')
+])
+
+model.compile(optimizer=Adam(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model with error handling
+try:
+    model.fit(X, y, epochs=200, batch_size=32, verbose=1)
+except Exception as e:
+    print(f"Error during model training: {str(e)}")
+    print(f"X shape: {X.shape}, y shape: {y.shape}")
+    raise
+
+# Chatbot function
+def chatbot_response(user_input):
+    processed_input = preprocess_text(user_input)
+    input_vector = vectorizer.transform([processed_input]).toarray()
+
+    # Find similar responses from the dataset
+    similarities = cosine_similarity(input_vector, tfidf_matrix)
+    most_similar_idx = similarities.argmax()
+    response = df.iloc[most_similar_idx][text_column]
+
+    # Classify intent
+    intent_probs = model.predict(input_vector)[0]
+    intent_idx = intent_probs.argmax()
+    intent_tag = list(pd.get_dummies([i['tag'] for i in intents['intents']]).columns)[intent_idx]
+
+    # Get response from intents
+    for intent in intents['intents']:
+        if intent['tag'] == intent_tag:
+            intent_response = np.random.choice(intent['responses'])
+            break
+
+    return f"Dataset Response: {response}\n\nIntent Response: {intent_response}"
+
+# Main chat loop
+print("Mental Health Chatbot: Hello! I'm here to provide support and resources for mental health. How can I help you today?")
+while True:
+    user_input = input("You: ")
+    if user_input.lower() in ['quit', 'exit', 'bye']:
+        print("Mental Health Chatbot: Take care! Remember, it's okay to seek help when you need it.")
+        break
+    response = chatbot_response(user_input)
+    print("Mental Health Chatbot:", response)
+

Mental Health chatbot/README_BOT.md

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+# Mental Health Chatbot
+
+This project implements a chatbot designed to provide support and information related to mental health. It uses natural language processing and machine learning techniques to understand user input and generate appropriate responses.
+
+## Features
+
+- Sentiment analysis of user input
+- Personalized responses based on user's emotional state
+- Information provision on various mental health topics
+- Supportive and empathetic conversation
+
+## Installation
+
+1. Clone this repository:
+   ```
+   git clone https://github.com/Ashutoshdas-dev/All-In-One-Python-Projects
+   cd All-In-One-Python-Projects/Mental Health Chatbot
+   ```
+
+2. Set up a virtual environment:
+   ```
+   python -m venv venv
+   ```
+
+3. Activate the virtual environment:
+   - On Windows:
+     ```
+     venv\Scripts\activate
+     ```
+   - On macOS and Linux:
+     ```
+     source venv/bin/activate
+     ```
+
+4. Install the required dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+1. Ensure your virtual environment is activated (see step 3 in Installation).
+
+2. Run the main script:
+   ```
+   python mental_health_chatbot.py
+   ```
+
+3. Follow the prompts to interact with the chatbot.
+
+4. Type 'quit' or 'exit' to end the conversation.
+
+5. When you're done, deactivate the virtual environment:
+   ```
+   deactivate
+   ```
+
+## Datasets
+
+This project utilizes the following datasets for training and improving the chatbot's responses:
+
+1. [Sentiment Analysis for Mental Health](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)
+   - Description: This dataset contains text data labeled with sentiment for mental health-related content.
+   - Use: Helps in training the sentiment analysis component of the chatbot.
+
+2. [Mental Health Conversational Data](https://www.kaggle.com/datasets/elvis23/mental-health-conversational-data)
+   - Description: This dataset includes conversations related to mental health topics.
+   - Use: Aids in training the chatbot to generate more natural and context-appropriate responses.
+
+

Mental Health chatbot/requirements.txt

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+pandas
+numpy
+nltk
+scikit-learn
+tensorflow