Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from datasets import load_dataset
+from sklearn.metrics import precision_score, recall_score
+import pandas as pd
+import os
+
+# Load SBERT model
+model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+
+# Load MS MARCO dataset (10,000 passages for demo)
+dataset = load_dataset("ms_marco", "passage", split="train[:10000]")
+passages = dataset["passage"]
+passage_embeddings = model.encode(passages, convert_to_numpy=True, show_progress_bar=True)
+
+# Build FAISS index
+dimension = passage_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(passage_embeddings)
+
+# CSV log file
+log_file = "results_log.csv"
+if not os.path.exists(log_file):
+    pd.DataFrame(columns=["Query", "Relevant Passage", "Precision@10", "Recall@10", "F1@10", "MRR", "nDCG@10"]).to_csv(log_file, index=False)
+
+# Search function
+def semantic_search(query, k=10):
+    query_vec = model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_vec, k)
+    results = [passages[i] for i in indices[0]]
+    return "\n\n".join(results)
+
+# Helper functions for metrics
+def mean_reciprocal_rank(y_true):
+    for rank, rel in enumerate(y_true, start=1):
+        if rel == 1:
+            return 1 / rank
+    return 0
+
+def ndcg_at_k(y_true, k=10):
+    y_true = np.array(y_true)[:k]
+    if y_true.sum() == 0:
+        return 0.0
+    dcg = np.sum((2**y_true - 1) / np.log2(np.arange(2, len(y_true) + 2)))
+    ideal = np.sort(y_true)[::-1]
+    idcg = np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))
+    return dcg / idcg if idcg > 0 else 0.0
+
+# Evaluation function
+def evaluate(query, relevant_passage, k=10):
+    results_text = semantic_search(query, k)
+    results_list = results_text.split("\n\n")
+
+    y_true = [1 if relevant_passage in r else 0 for r in results_list]
+    y_pred = [1] * len(results_list)
+
+    precision = precision_score(y_true, y_pred, zero_division=0)
+    recall = recall_score(y_true, y_pred, zero_division=0)
+    f1 = 2 * (precision * recall) / (precision + recall + 1e-9)
+    mrr = mean_reciprocal_rank(y_true)
+    ndcg = ndcg_at_k(y_true, k)
+
+    # Log results to CSV
+    df = pd.read_csv(log_file)
+    new_row = {
+        "Query": query,
+        "Relevant Passage": relevant_passage,
+        "Precision@10": round(precision, 3),
+        "Recall@10": round(recall, 3),
+        "F1@10": round(f1, 3),
+        "MRR": round(mrr, 3),
+        "nDCG@10": round(ndcg, 3),
+    }
+    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+    df.to_csv(log_file, index=False)
+
+    return new_row, f"📊 {len(df)} evaluations logged so far."
+
+# Function to download CSV log
+def download_log():
+    return log_file
+
+# Function to check current log count
+def check_log_count():
+    df = pd.read_csv(log_file)
+    return f"📊 {len(df)} evaluations logged so far."
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔎 Semantic Search with SBERT (MS MARCO Subset)")
+
+    with gr.Row():
+        query_input = gr.Textbox(label="Enter your search query")
+        k_input = gr.Slider(1, 20, value=10, step=1, label="Top-K Results")
+
+    results_output = gr.Textbox(label="Search Results", lines=10)
+    run_btn = gr.Button("Search")
+
+    run_btn.click(fn=semantic_search, inputs=[query_input, k_input], outputs=results_output)
+
+    gr.Markdown("### 📊 Evaluation")
+    with gr.Row():
+        eval_query = gr.Textbox(label="Evaluation Query")
+        relevant_passage = gr.Textbox(label="Known Relevant Passage")
+    eval_btn = gr.Button("Run Evaluation")
+    eval_output = gr.JSON(label="Evaluation Metrics")
+    eval_counter = gr.Label(label="Evaluation Log Count")
+
+    eval_btn.click(fn=evaluate, inputs=[eval_query, relevant_passage, k_input], outputs=[eval_output, eval_counter])
+
+    gr.Markdown("### 📂 Download Logged Results")
+    download_btn = gr.Button("Download CSV")
+    file_output = gr.File()
+    count_btn = gr.Button("Check Log Count")
+    count_output = gr.Label(label="Evaluation Log Count")
+
+    download_btn.click(fn=download_log, outputs=file_output)
+    count_btn.click(fn=check_log_count, outputs=count_output)
+
+demo.launch()