Update music_recommendations.py

music_recommendations.py

@@ -1,59 +1,59 @@
-import streamlit as st
-from transformers import pipeline
-import scipy.io.wavfile
-from openai import OpenAI
-import time
-import numpy as np
-
-# Initialize the OpenAI client
-client = OpenAI(
-    api_key="a99ae8e15f1e439a935b5e1cf2005c8b",
-    base_url="https://api.aimlapi.com",
-)
-
-# Streamlit app layout
-st.title("Mood-based Music Generator")
-
-# Ask the user for their feeling and preferred music style via Streamlit inputs
-user_feeling = st.text_input("How are you feeling right now?", value="feeling down")
-music_style = st.text_input("What music style do you prefer?", value="pop")
-
-# Button to trigger music generation
-if st.button("Generate Music"):
-    with st.spinner("Generating music, please wait..."):
-        # Send the feeling and music style to the OpenAI model
-        response = client.chat.completions.create(
-            model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are a musical assistant that, based on a user's feeling, can describe it as a musical instrument. Provide a short one-sentence response."
-                },
-                {
-                    "role": "user",
-                    "content": f"I am feeling {user_feeling}. Can you make me happy with a {music_style} style of music?"
-                },
-            ],
-        )
-
-        message = response.choices[0].message.content
-        st.write(f"Assistant: {message}")
-
-        # Load the synthesizer model for music generation
-        synthesiser = pipeline("text-to-audio", "facebook/musicgen-small")
-
-        # Simulate a short wait to represent loading time for music generation
-        time.sleep(2)
-
-        # Generate the music using the synthesizer model based on the message
-        music = synthesiser(message, forward_params={"do_sample": True, "guidance_scale": 1})
-
-        # Save the generated audio to a file
-        audio_filename = "musicgen_out.wav"
-        scipy.io.wavfile.write(audio_filename, rate=music["sampling_rate"], data=np.array(music["audio"]))
-
-        st.success("Music has been generated!")
-
-        # Play the generated audio in Streamlit
-        st.audio(audio_filename)
-
+import streamlit as st
+from transformers import pipeline
+import scipy.io.wavfile
+from openai import OpenAI
+import time
+import numpy as np
+
+# Initialize the OpenAI client
+client = OpenAI(
+    api_key="a99ae8e15f1e439a935b5e1cf2005c8b",
+    base_url="https://api.aimlapi.com",
+)
+
+# Streamlit app layout
+st.title("Mood-based Music Generator")
+
+# Ask the user for their feeling and preferred music style via Streamlit inputs
+user_feeling = st.text_input("How are you feeling right now?", value="feeling down")
+music_style = st.text_input("What music style do you prefer?", value="pop")
+
+# Button to trigger music generation
+if st.button("Generate Music"):
+    with st.spinner("Generating music, please wait..."):
+        # Send the feeling and music style to the OpenAI model
+        response = client.chat.completions.create(
+            model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a musical assistant that, based on a user's feeling, can describe it as a musical instrument. Provide a short one-sentence response."
+                },
+                {
+                    "role": "user",
+                    "content": f"I am feeling {user_feeling}. Can you make me happy with a {music_style} style of music?"
+                },
+            ],
+        )
+
+        message = response.choices[0].message.content
+        st.write(f"Assistant: {message}")
+
+        # Load the synthesizer model for music generation
+        synthesiser = pipeline("text-to-audio", "facebook/musicgen-small")
+
+        # Simulate a short wait to represent loading time for music generation
+        time.sleep(2)
+
+        # Generate the music using the synthesizer model based on the message
+        music = synthesiser(message, forward_params={"do_sample": True, "guidance_scale": 1})
+
+        # Save the generated audio to a file
+        audio_filename = "musicgen_out.wav"
+        scipy.io.wavfile.write(audio_filename, rate=music["sampling_rate"], data=np.array(music["audio"]))
+
+        st.success("Music has been generated!")
+
+        # Play the generated audio in Streamlit
+        st.audio(audio_filename)
+