Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import pandas as pd
+from datasets import load_dataset
+from annoy import AnnoyIndex
+import numpy as np
+
+# Load the dataset
+dataset = load_dataset("nickprock/AIRC_FAQ")
+df = pd.DataFrame(dataset["train"])
+
+# Extract questions and answers
+questions = df["question"].tolist()
+answers = df["answer"].tolist()
+
+# Sentence-transformers models to test
+model_names = [
+    "nickprock/multi-sentence-BERTino",
+    "nickprock/sentence-bert-base-italian-uncased",
+    "nickprock/sentence-bert-base-italian-xxl-uncased",
+    "nickprock/mmarco-bert-base-italian-uncased",
+]
+
+models = {name: SentenceTransformer(name) for name in model_names}
+annoy_indexes = {}  # Store Annoy indexes for each model
+
+def build_annoy_index(model_name):
+    """Builds an Annoy index for a given model."""
+    model = models[model_name]
+    embeddings = model.encode(answers)
+    embedding_dim = embeddings.shape[1]
+    annoy_index = AnnoyIndex(embedding_dim, "angular")  # Use angular distance for cosine similarity
+    for i, embedding in enumerate(embeddings):
+        annoy_index.add_item(i, embedding)
+    annoy_index.build(10)  # Build with 10 trees
+    return annoy_index
+
+# Build Annoy indexes for each model
+for model_name in model_names:
+    annoy_indexes[model_name] = build_annoy_index(model_name)
+
+def find_similar_answer_annoy(question, model_name):
+    """Finds the most similar answer using Annoy."""
+    model = models[model_name]
+    annoy_index = annoy_indexes[model_name]
+    question_embedding = model.encode(question)
+    nearest_neighbors = annoy_index.get_nns_by_vector(question_embedding, 1)  # Get the nearest neighbor
+    best_answer_index = nearest_neighbors[0]
+    return answers[best_answer_index]
+
+def compare_models_annoy(question, model1_name, model2_name, model3_name, model4_name):
+    """Compares the results of different models using Annoy."""
+    answer1 = find_similar_answer_annoy(question, model1_name)
+    answer2 = find_similar_answer_annoy(question, model2_name)
+    answer3 = find_similar_answer_annoy(question, model3_name)
+    answer4 = find_similar_answer_annoy(question, model4_name)
+    return answer1, answer2, answer3, answer4
+
+iface = gr.Interface(
+    fn=compare_models_annoy,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your question here..."),
+        gr.Dropdown(model_names, value=model_names[0], label="Model 1"),
+        gr.Dropdown(model_names, value=model_names[1], label="Model 2"),
+        gr.Dropdown(model_names, value=model_names[2], label="Model 3"),
+        gr.Dropdown(model_names, value=model_names[3], label="Model 4"),
+    ],
+    outputs=[
+        gr.Textbox(label=model_names[0]),
+        gr.Textbox(label=model_names[1]),
+        gr.Textbox(label=model_names[2]),
+        gr.Textbox(label=model_names[3]),
+    ],
+    title="Sentence Transformer Model Comparison (Annoy)",
+    description="Enter a question and compare the answers generated by different sentence-transformer models (using Annoy for faster search).",
+)
+
+iface.launch()