Merge branch 'king04aman:main' into Word_Frequency_Counter

.github/workflows/welcome.yml

@@ -1,40 +1,80 @@
-name: New Contributor Welcome
+name: Welcome Comments
 
 permissions:
   actions: write
+  attestations: write
   checks: write
   contents: write
+  deployments: write
+  id-token: write
   issues: write
+  discussions: write
+  packages: write
+  pages: write
   pull-requests: write
+  repository-projects: write
+  security-events: write
+  statuses: write
 
 on:
-  pull_request:
-    types: [opened, closed]
   issues:
-    types: [opened]
+    types: [opened, closed]
+  pull_request_target:
+    types: [opened, closed]
 
 jobs:
-  greet_new_contributor:
+  welcomer:
     runs-on: ubuntu-latest
     steps:
-      - uses: bubkoo/welcome-action@v1
+      - name: Auto Welcome on Issues or PRs
+        uses: actions/github-script@v6
         with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          FIRST_ISSUE_REACTIONS: "+1, hooray, rocket, heart"
-          FIRST_ISSUE: >
-            👋 Greetings @{{ author }}!
-
-            We're thrilled to see you opening your first issue! Your input is invaluable to us. Don’t forget to adhere to our issue template for the best experience.
-
-          FIRST_PR: >
-            👋 Welcome aboard, @{{ author }}!
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            const author = context.payload.sender.login;
+            const commentBody = (message) => `👋 @${author} 👋\n\n${message}`;
 
-            We're delighted to have your first pull request! Please take a moment to check our contributing guidelines to ensure a smooth process.
+            if (context.eventName === 'issues') {
+              const issue = context.payload.issue;
 
-          FIRST_PR_MERGED: >
-            🎉 Kudos @{{ author }}!
+              if (context.payload.action === 'opened') {
+                const message = `We're thrilled to see you opening an issue! Your input is valuable to us. Don’t forget to fill out our issue template for the best experience. We will look into it soon.`;
+                github.rest.issues.createComment({
+                  issue_number: issue.number,
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  body: commentBody(message),
+                });
+              } else if (context.payload.action === 'closed') {
+                const message = `Thanks for closing the issue! We appreciate your updates.`;
+                github.rest.issues.createComment({
+                  issue_number: issue.number,
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  body: commentBody(message),
+                });
+              }
+            } else if (context.eventName === 'pull_request_target') {
+              const pr = context.payload.pull_request;
 
-            You've just merged your first pull request! We're excited to have you in our community. Keep up the fantastic contributions!
-          STAR_MESSAGE: If you enjoy this project, please consider ⭐ starring ⭐ this repository!
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+              if (context.payload.action === 'opened') {
+                const message = `We're delighted to have your pull request! Please take a moment to check our contributing guidelines and ensure you've filled out the PR template for a smooth process. We will review it soon.`;
+                github.rest.issues.createComment({
+                  issue_number: pr.number,
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  body: commentBody(message),
+                });
+              } else if (context.payload.action === 'closed') {
+            		const message = pr.merged 
+                  ? `🎉 You've just merged your pull request! We're excited to have you in our community. Keep up the fantastic contributions to the project!`
+                  : `Thanks for closing the pull request! Your contributions are valuable to us.`;
+                
+                github.rest.issues.createComment({
+                  issue_number: pr.number,
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  body: commentBody(message),
+                });
+              }
+            }

Mental Health chatbot/Mental_health_bot.py

@@ -0,0 +1,125 @@
+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

@@ -0,0 +1,70 @@
+# 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

@@ -0,0 +1,5 @@
+pandas
+numpy
+nltk
+scikit-learn
+tensorflow

My-Personal-Journal/README.md

@@ -0,0 +1,34 @@
+# Personal Journal
+
+A simple web-based application for maintaining a personal journal. Users can create entries, tag them, and search through past entries by date or keywords. The application is built using Flask and SQLite for a lightweight and efficient experience.
+
+## Features
+
+- **Add New Entries**: Users can add journal entries with mood, content, and tags.
+- **Search Entries**: Search through entries using keywords or specific dates.
+- **Tag Management**: Create and view tags associated with each entry, and filter entries by tags.
+- **User-Friendly Interface**: A clean and professional UI for easy navigation and use.
+
+## Technologies Used
+
+- Python
+- Flask
+- SQLite
+- HTML/CSS
+
+## Installation
+
+1. **Clone the Repository**:
+   ```bash
+   git clone <repository-url>
+   cd your_project
+
+2. **Install Required Packages: Make sure you have Python installed (preferably Python 3). Install the required packages using pip**:
+    ```bash
+    pip install -r requirements.txt
+
+3. **Run the Application: Start the Flask application**:
+    ```bash
+    python app.py
+
+4. **Access the App: Open your web browser and navigate to http://127.0.0.1:8080 to access the application.**