Merge pull request #741 from ananyag309/branch

Added Post Traumatic Stress Prediction

Post Traumatic Stress Prediction/Dataset/readme.md

@@ -0,0 +1,3 @@
+The link to the dataset is given below :-
+
+# LINK:  https://www.kaggle.com/datasets/mahek6114/post-traumatic-stress-disorder-detection

Post Traumatic Stress Prediction/Models/main.py

@@ -0,0 +1,42 @@
+import os
+import numpy as np
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+
+# Define paths
+working_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = f"{working_dir}/trained_model/PTSD_Detection_Model.h5"
+model = load_model(model_path)
+
+# Preprocessing function
+def preprocess_text(text):
+    # Tokenize text
+    tokenizer = Tokenizer(num_words=5000)
+    tokenizer.fit_on_texts(text)
+    sequences = tokenizer.texts_to_sequences(text)
+    padded_sequences = pad_sequences(sequences, maxlen=100)
+    return padded_sequences
+
+# Streamlit app
+st.title('🧠 PTSD Detection')
+uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
+
+if uploaded_file is not None:
+    # Read the text file
+    text_data = uploaded_file.read().decode("utf-8")
+    text_data = [text_data]
+
+    # Preprocess the text data
+    with st.spinner('Processing text...'):
+        preprocessed_text = preprocess_text(text_data)
+        predicted_probabilities = model.predict(preprocessed_text)
+        predicted_class_index = np.argmax(predicted_probabilities)
+
+        # Map class index to PTSD status
+        class_mapping = {0: "No PTSD", 1: "PTSD"}
+        predicted_class = class_mapping[int(predicted_class_index)]
+
+    st.write(f"Predicted PTSD status: {predicted_class}")

Post Traumatic Stress Prediction/Models/train.py

@@ -0,0 +1,50 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense
+
+# Load and preprocess data
+def load_data(data_dir):
+    texts, labels = [], []
+    for label, category in enumerate(['without_ptsd', 'with_ptsd']):
+        category_dir = os.path.join(data_dir, category)
+        for filename in os.listdir(category_dir):
+            with open(os.path.join(category_dir, filename), 'r') as file:
+                texts.append(file.read())
+                labels.append(label)
+    return texts, np.array(labels)
+
+data_dir = 'dataset'
+texts, labels = load_data(data_dir)
+
+# Tokenize and pad sequences
+tokenizer = Tokenizer(num_words=5000)
+tokenizer.fit_on_texts(texts)
+sequences = tokenizer.texts_to_sequences(texts)
+padded_sequences = pad_sequences(sequences, maxlen=100)
+
+# Split data into training and test sets
+split_idx = int(0.75 * len(padded_sequences))
+x_train, x_test = padded_sequences[:split_idx], padded_sequences[split_idx:]
+y_train, y_test = labels[:split_idx], labels[split_idx:]
+
+# Build the model
+model = Sequential([
+    Embedding(input_dim=5000, output_dim=128, input_length=100),
+    Conv1D(filters=64, kernel_size=5, activation='relu'),
+    GlobalMaxPooling1D(),
+    Dense(10, activation='relu'),
+    Dense(1, activation='sigmoid')
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))
+
+# Save the model
+model.save(model_path)

Post Traumatic Stress Prediction/README.md

@@ -0,0 +1,39 @@
+## Overview 
+This project utilizes a Convolutional Neural Network (CNN) to detect Post-Traumatic Stress Disorder (PTSD) from textual data. The model is trained on a dataset containing text samples from individuals diagnosed with PTSD and those without the condition.
+
+## Dataset
+The dataset is divided into two categories:
+
+with_ptsd: Text samples from individuals diagnosed with PTSD.
+<br>
+without_ptsd: Text samples from individuals without PTSD.
+
+## Model Architecture
+The model consists of the following layers:
+
+- Embedding layer for text data
+- Convolutional layers with ReLU activation and MaxPooling
+- Flatten layer
+- Fully connected Dense layers with Dropout
+- Output layer with Sigmoid activation
+
+## Results
+The model achieves good accuracy in detecting PTSD, as demonstrated by the classification report and accuracy score.
+
+## Usage
+To train the model and make predictions, run the provided script. The trained model will be saved as ptsd_detection_model.h5.
+
+## Requirements
+- numpy
+- pandas
+- tensorflow
+- keras
+- sklearn
+
+## How to Run
+Ensure you have the required libraries installed.
+Prepare your dataset in the specified format.
+Run the script to train the model and make predictions.
+
+## Display Predictions
+The script includes a function to evaluate the model on test data, displaying classification metrics such as precision, recall, and F1 score.

Post Traumatic Stress Prediction/requirements.txt

@@ -0,0 +1,8 @@
+**Requirements For Project :-**
+
+1. NumPy: Fundamental package for numerical computing.
+2. pandas: Data analysis and manipulation library.
+3. scikit-learn: Machine learning library for classification, regression, and clustering.
+4.  Matplotlib: Plotting library for creating visualizations.
+5. tqdm: Progress bar utility for tracking iterations.
+6. seaborn: Statistical data visualization library based on Matplotlib.