Integrated features for depression detection

DepressionDetectionSystem/depressionDetectionSystem.py

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+import streamlit as st
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torchaudio
+import librosa
+import numpy as np
+from PIL import Image
+
+def load_text_model():
+    model_name = "distilbert-base-uncased"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)  # 0: Not Depressed, 1: Depressed
+    return model, tokenizer
+
+def predict_text_depression(text, model, tokenizer):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    return probs[0][1].item()  # Probability of being depressed
+
+def analyze_audio(audio_file):
+    y, sr = librosa.load(audio_file, sr=16000)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    mean_mfccs = np.mean(mfccs, axis=1)
+    score = np.mean(mean_mfccs) / 100  # Basic normalization
+    return min(max(score, 0), 1)  # Ensure within [0,1]
+
+def analyze_image(image_file):
+    # You can add your image processing code here (e.g., face emotion detection)
+    image = Image.open(image_file)
+    # Example of displaying the image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    # For simplicity, returning a mock analysis score
+    return 0.7  # This can be changed with actual image processing logic
+
+st.title("Depression Detection System")
+
+# Load text model
+model, tokenizer = load_text_model()
+
+# Text input for depression detection
+text_input = st.text_area("Enter your thoughts:")
+if st.button("Analyze Text"):
+    if text_input:
+        depression_score = predict_text_depression(text_input, model, tokenizer)
+        st.write(f"Depression Probability: {depression_score:.2f}")
+    else:
+        st.warning("Please enter text.")
+
+# Audio input for depression detection
+uploaded_audio = st.file_uploader("Upload an audio file (WAV, MP3)", type=["wav", "mp3"])
+if uploaded_audio and st.button("Analyze Audio"):
+    audio_score = analyze_audio(uploaded_audio)
+    st.write(f"Depression Probability (from audio): {audio_score:.2f}")
+
+# Image input for depression detection (PNG, JPEG, JPG)
+uploaded_image = st.file_uploader("Upload an image (PNG, JPEG, JPG)", type=["png", "jpeg", "jpg"])
+if uploaded_image and st.button("Analyze Image"):
+    image_score = analyze_image(uploaded_image)
+    st.write(f"Depression Probability (from image): {image_score:.2f}")
+