Add gradio application

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+
+from utils.dataset_loader import DatasetLoader
+from embeddings.huggingface import HuggingFaceEncoder
+from search.faiss import FaissSearchEngine
+
+# Preload dataset with embeddings for chunk_size = 25
+ds_test_embeddings = DatasetLoader.load_from_file_with_embeddings("./data/df_chunked_25_with_embeddings.csv")
+hf_encoder = HuggingFaceEncoder("sentence-transformers/multi-qa-mpnet-base-dot-v1")
+
+
+def retrieve_chunks(query, chunk_size, embeddings_generator, retriever_method, num_chunks_to_retrieve):
+    # Ignore chunk_size, embeddings_generator, and retriever_method,
+    # as we currently support only a single configuration
+    faiss_search = FaissSearchEngine(ds_test_embeddings, hf_encoder)
+
+    return faiss_search.search(query, num_chunks_to_retrieve)
+
+
+# Create the Gradio application
+with gr.Blocks() as demo:
+    query = gr.inputs.Textbox(label='Query', placeholder="Enter your query here. Example: 'What is a transformer?'")
+    chunk_size = gr.inputs.Slider(
+        minimum=25,
+        maximum=25,
+        step=25,
+        default=25,
+        label='Chunk Size'
+    )
+    embeddings_generator = gr.Radio(
+        ['sentence-transformers/multi-qa-mpnet-base-dot-v1'],
+        label='Embeddings Generator',
+        value='sentence-transformers/multi-qa-mpnet-base-dot-v1'
+    )
+    retriever_method = gr.Radio(
+        ['FAISS'],
+        value="FAISS",
+        label="Retriever Method"
+    )
+    num_chunks_to_retrieve = gr.inputs.Slider(
+        minimum=3,
+        maximum=5,
+        step=1,
+        default=3,
+        label='Number of Chunks to Retrieve'
+    )
+    inputs = [query, chunk_size, embeddings_generator, retriever_method, num_chunks_to_retrieve]
+
+    submit_btn = gr.Button("Submit")
+
+    outputs = gr.Dataframe(
+        headers=['id', 'guest', 'title', 'text', 'start', 'end', 'scores'],
+        type="pandas",
+        wrap=True
+    )
+
+    submit_btn.click(retrieve_chunks, inputs=inputs, outputs=outputs)
+
+# Run the Gradio application
+demo.launch()