Create app.py

app.py

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+import gradio as gr
+import torch
+import pickle
+from transformers import ClapModel, ClapProcessor
+from sklearn.metrics.pairwise import cosine_similarity
+
+def load_results_from_pickle(input_file):
+    with open(input_file, 'rb') as f:
+        return pickle.load(f)
+
+def compare_text_to_audio_embeddings(text, pickle_file):
+    model = ClapModel.from_pretrained("laion/larger_clap_music_and_speech").to(0)
+    processor = ClapProcessor.from_pretrained("laion/larger_clap_music_and_speech")
+
+    # Generate text embedding
+    text_inputs = processor(text=text, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        text_embedding = model.get_text_features(**text_inputs.to(0))
+    text_embedding = text_embedding.cpu().numpy()
+
+    # Load audio embeddings
+    audio_embeddings = load_results_from_pickle(pickle_file)
+
+    # Compare embeddings
+    similarities = []
+    for item in audio_embeddings:
+        similarity = cosine_similarity(text_embedding, item['embedding'])[0][0]
+        similarities.append((item['filename'], item["url"], similarity))
+
+    # Sort by similarity (highest first)
+    similarities.sort(key=lambda x: x[2], reverse=True)
+
+    return similarities
+
+def get_matches(text_query):
+    matches = compare_text_to_audio_embeddings(text_query, "audio_embeddings_v3.pkl")
+    
+    # Format the output
+    output = f"Top 5 matches for '{text_query}':\n\n"
+    for filename, url, similarity in matches[:5]:
+        output += f"{filename}, {url}: {similarity:.4f}\n"
+    
+    return output
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to Audio Comparison")
+    with gr.Row():
+        text_input = gr.Textbox(label="Enter your text query")
+        output = gr.Textbox(label="Results", lines=10)
+    submit_button = gr.Button("Submit")
+    submit_button.click(fn=get_matches, inputs=text_input, outputs=output)
+
+# Launch the app
+demo.launch()