Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load pre-trained model and tokenizer
+model_name = "distilbert-base-uncased"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define a function to search for Samba songs
+def search_samba(query):
+    # Tokenize the query
+    inputs = tokenizer(query, return_tensors="pt")
+
+    # Get the model's output
+    outputs = model(**inputs)
+
+    # Get the predicted class probabilities
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=1)
+
+    # Return the top 5 Samba song matches
+    top_5 = torch.topk(probs, k=5)
+    return top_5.indices, top_5.values
+
+# Create a Streamlit app
+st.title("Samba Search")
+
+# Get user input
+query = st.text_input("Enter a song title or artist")
+
+# Search for Samba songs
+if st.button("Search"):
+    indices, values = search_samba(query)
+
+    # Display the top 5 matches
+    st.write("Top 5 Samba Song Matches:")
+    for i, (index, value) in enumerate(zip(indices[0], values[0])):
+        st.write(f"{i+1}. Song {index.item()} - Probability: {value.item():.2f}")