Updating main

app.py

@@ -1,13 +1,46 @@
 import streamlit as st
 from transformers import pipeline
+import scipy.io.wavfile
+import tempfile
 
-model_name = "SamLowe/roberta-base-go_emotions"
 
-pipe = pipeline(task="text-classification", model=model_name)
+# -----------------------------------------------------------
+def load_css(file_path):
+    with open(file_path) as f:
+        css_to_load = f.read()
+        st.markdown(f"<style>{css_to_load}</style>", unsafe_allow_html=True)
 
-user_text = st.text_area("Explain your feelings here")
 
-if user_text:
-    emotion = pipe(user_text)
-    st.markdown("You are feeling like ")
-    st.json(emotion)
+# -----------------------------------------------------------
+def load_model():
+    return pipeline("text-to-audio", model="facebook/musicgen-small")
+
+
+# -----------------------------------------------------------
+def main():
+    try:
+        st.set_page_config(page_title="Text to Music", page_icon="Music")
+        load_css("styles/styles.css")
+        st.title("Turn Your Text Into Music")
+
+        synthesizer = load_model()
+        user_text = st.text_area("What type of beat would you like to hear?")
+
+        if user_text:
+            music = synthesizer(user_text, forward_params={"do_sample": True})
+
+            with tempfile.NamedTemporaryFile(delete=True, suffix=".wav") as tmp:
+                print(f"Temporary file created at: {tmp.name}")
+                scipy.io.wavfile.write(
+                    tmp.name, rate=music["sampling_rate"], data=music["audio"]
+                )
+                st.audio(tmp.name, format="audio/wav", start_time=0)
+
+    except Exception as e:
+        st.error(e)
+
+
+# -----------------------------------------------------------
+
+if __name__ == "__main__":
+    main()