Update app.py

app.py

@@ -1,134 +1,146 @@
 import gradio as gr
 import joblib
 import numpy as np
+import spotipy
+from spotipy.oauth2 import SpotifyClientCredentials
+import random
 
 # Load the saved models and pipelines
 anxiety_model = joblib.load('Anxiety_best_model.pkl')
 anxiety_pipeline = joblib.load('Anxiety_best_pipeline.pkl')
-
 depression_model = joblib.load('Depression_best_model.pkl')
 depression_pipeline = joblib.load('Depression_best_pipeline.pkl')
-
 insomnia_model = joblib.load('Insomnia_best_model.pkl')
 insomnia_pipeline = joblib.load('Insomnia_best_pipeline.pkl')
-
 ocd_model = joblib.load('OCD_best_model.pkl')
 ocd_pipeline = joblib.load('OCD_best_pipeline.pkl')
 
+# Spotify API credentials
+SPOTIPY_CLIENT_ID = '79d5de7b9bec45c4bc2ae857e29d89e4'
+SPOTIPY_CLIENT_SECRET = '410907e455a24118810bd89ee99d6f68'
+
+# Initialize Spotify client
+sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(client_id=SPOTIPY_CLIENT_ID,
+                                                           client_secret=SPOTIPY_CLIENT_SECRET))
+
 # Define the prediction functions
-def Anxiety_predict(input1, input2, input3, input4, input5, input6):
-    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
-    inputs_scaled = anxiety_pipeline.transform(inputs)
-    prediction = anxiety_model.predict(inputs_scaled)
-    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+def predict(model, pipeline, inputs):
+    inputs_array = np.array(inputs).reshape(1, -1)
+    inputs_scaled = pipeline.transform(inputs_array)
+    prediction = model.predict(inputs_scaled)
+    return prediction[0]
 
-def Depression_predict(input1, input2, input3, input4, input5, input6):
-    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
-    inputs_scaled = depression_pipeline.transform(inputs)
-    prediction = depression_model.predict(inputs_scaled)
-    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+# Function to recommend songs based on the condition and prediction
+def recommend_songs(condition, prediction):
+    mood_keywords = {
+        "Anxiety": ["relaxing", "calming", "soothing"],
+        "Depression": ["uplifting", "motivational", "cheerful"],
+        "Insomnia": ["sleep", "ambient", "white noise"],
+        "OCD": ["focus", "mindfulness", "meditation"]
+    }
+    
+    # Select a random keyword based on the condition
+    keyword = random.choice(mood_keywords[condition])
+    
+    # Adjust the search query based on the prediction value
+    if prediction < 3:
+        query = f"mild {keyword} music"
+    elif prediction < 6:
+        query = f"moderate {keyword} music"
+    else:
+        query = f"intense {keyword} music"
 
-def Insomnia_predict(input1, input2, input3, input4, input5, input6):
-    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
-    inputs_scaled = insomnia_pipeline.transform(inputs)
-    prediction = insomnia_model.predict(inputs_scaled)
-    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+    results = sp.search(q=query, type='track', limit=5)
+    songs = []
+    for item in results['tracks']['items']:
+        song = {
+            'name': item['name'],
+            'artist': item['artists'][0]['name'],
+            'album': item['album']['name'],
+            'image_url': item['album']['images'][0]['url'] if item['album']['images'] else None,
+            'preview_url': item['preview_url']
+        }
+        songs.append(song)
+    return songs
 
-def OCD_predict(input1, input2, input3, input4, input5, input6):
-    inputs = np.array([input1, input2, input3, input4, input5, input6]).reshape(1, -1)
-    inputs_scaled = ocd_pipeline.transform(inputs)
-    prediction = ocd_model.predict(inputs_scaled)
-    return prediction[0], f"The predicted value is {prediction[0]:.2f}"
+# Define the main prediction and recommendation function
+def predict_and_recommend(condition, *inputs):
+    if condition == "Anxiety":
+        prediction = predict(anxiety_model, anxiety_pipeline, inputs)
+    elif condition == "Depression":
+        prediction = predict(depression_model, depression_pipeline, inputs)
+    elif condition == "Insomnia":
+        prediction = predict(insomnia_model, insomnia_pipeline, inputs)
+    else:  # OCD
+        prediction = predict(ocd_model, ocd_pipeline, inputs)
+    
+    songs = recommend_songs(condition, prediction)
+    
+    return prediction, f"The predicted {condition} level is {prediction:.2f}", songs
 
-# Define the Gradio interfaces
-anxiety_iface = gr.Interface(
-    fn=Anxiety_predict,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Number(label="Hours per day"),
-        gr.Number(label="Depression"),
-        gr.Number(label="Insomnia"),
-        gr.Number(label="OCD"),
-        gr.Number(label="BPM")
-    ],
-    outputs=[
-        gr.Number(label="Predicted Value"),
-        gr.Textbox(label="Prediction Description")
-    ],
-    title="Music & Mental Health Predictor - Anxiety",
-    description="Enter 5 numeric values to get a prediction.",
-    theme=gr.themes.Soft(),
-    examples=[[18, 3, 0, 1, 0, 156]]
-)
-depression_iface = gr.Interface(
-    fn=Depression_predict,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Number(label="Hours per day"),
-        gr.Number(label="Insomnia"),
-        gr.Number(label="Anxiety"),
-        gr.Number(label="OCD"),
-        gr.Number(label="BPM")
-    ],
-    outputs=[
-        gr.Number(label="Predicted Value"),
-        gr.Textbox(label="Prediction Description")
-    ],
-    title="Music & Mental Health Predictor - Depression",
-    description="Enter 5 numeric values to get a prediction.",
-    theme=gr.themes.Soft(),
-    examples=[[18, 3, 0, 1, 0, 156]]
-)
-insomnia_iface = gr.Interface(
-    fn=Insomnia_predict,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Number(label="Hours per day"),
-        gr.Number(label="Depression"),
-        gr.Number(label="Anxiety"),
-        gr.Number(label="OCD"),
-        gr.Number(label="BPM")
-    ],
-    outputs=[
-        gr.Number(label="Predicted Value"),
-        gr.Textbox(label="Prediction Description")
-    ],
-    title="Music & Mental Health Predictor - Insomnia",
-    description="Enter 5 numeric values to get a prediction.",
-    theme=gr.themes.Soft(),
-    examples=[[18, 3, 0, 1, 0, 156]]
-)
+# Function to create HTML for song recommendations
+def create_song_html(songs):
+    if not songs or not isinstance(songs, list):
+        return "No songs to display"
+    
+    html = "<div style='display: flex; flex-wrap: wrap; justify-content: space-around;'>"
+    for song in songs:
+        html += f"""
+        <div style='width: 200px; margin: 10px; text-align: center;'>
+            <img src='{song.get('image_url', '')}' style='width: 150px; height: 150px; object-fit: cover;'>
+            <h3>{song.get('name', 'Unknown')}</h3>
+            <p>{song.get('artist', 'Unknown Artist')}</p>
+            <p>{song.get('album', 'Unknown Album')}</p>
+            {f'<audio controls src="{song.get("preview_url", "")}"></audio>' if song.get('preview_url') else 'No preview available'}
+        </div>
+        """
+    html += "</div>"
+    return html
 
-ocd_iface = gr.Interface(
-    fn=OCD_predict,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Number(label="Hours per day"),
-        gr.Number(label="Insomnia"),
-        gr.Number(label="Anxiety"),
-        gr.Number(label="Depression"),
-        gr.Number(label="BPM")
-    ],
-    outputs=[
-        gr.Number(label="Predicted Value"),
-        gr.Textbox(label="Prediction Description")
-    ],
-    title="Music & Mental Health Predictor - OCD",
-    description="Enter 5 numeric values to get a prediction.",
-    theme=gr.themes.Soft(),
-    examples=[[18, 3, 0, 1, 0, 156]]
-)
+# Define the main prediction and recommendation function
+def predict_and_recommend(condition, *inputs):
+    if condition == "Anxiety":
+        prediction = predict(anxiety_model, anxiety_pipeline, inputs)
+    elif condition == "Depression":
+        prediction = predict(depression_model, depression_pipeline, inputs)
+    elif condition == "Insomnia":
+        prediction = predict(insomnia_model, insomnia_pipeline, inputs)
+    else:  # OCD
+        prediction = predict(ocd_model, ocd_pipeline, inputs)
+    
+    songs = recommend_songs(condition, prediction)
+    song_html = create_song_html(songs)
+    
+    return prediction, f"The predicted {condition} level is {prediction:.2f}", song_html
 
-# Create a tabbed interface
+# Define the Gradio interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    with gr.Tab("Anxiety"):
-        anxiety_iface.render()
-    with gr.Tab("Depression"):
-        depression_iface.render()
-    with gr.Tab("Insomnia"):
-        insomnia_iface.render()
-    with gr.Tab("OCD"):
-        ocd_iface.render()
+    gr.Markdown("# Music & Mental Health Predictor")
+    
+    with gr.Row():
+        condition = gr.Radio(["Anxiety", "Depression", "Insomnia", "OCD"], label="Select Condition")
+    
+    with gr.Row():
+        with gr.Column():
+            age = gr.Number(label="Age")
+            hours_per_day = gr.Number(label="Hours per day listening to music")
+            depression = gr.Number(label="Depression level (0-10)")
+            insomnia = gr.Number(label="Insomnia level (0-10)")
+            ocd = gr.Number(label="OCD level (0-10)")
+            bpm = gr.Number(label="Preferred BPM")
+        
+        with gr.Column():
+            prediction_value = gr.Number(label="Predicted Value")
+            prediction_text = gr.Textbox(label="Prediction Description")
+            song_recommendations = gr.HTML(label="Recommended Songs")
+    
+    predict_btn = gr.Button("Predict and Recommend Songs")
+    
+    predict_btn.click(
+        fn=predict_and_recommend,
+        inputs=[condition, age, hours_per_day, depression, insomnia, ocd, bpm],
+        outputs=[prediction_value, prediction_text, song_recommendations],
+    )
 
 # Launch the interface
 demo.launch(debug=True)