Ganti keseluruhan UI untuk menggunakan model tersedia text ranking

src/streamlit_app.py

@@ -1,40 +1,68 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# Install paket yang dibutuhkan (jalankan ini di Colab atau lokal)
+!pip install streamlit sentence-transformers transformers
+
+import time
 import streamlit as st
+import torch
+from sentence_transformers import SentenceTransformer, CrossEncoder
+
+# Load Bi-Encoder dan Cross-Encoder
+bi_encoder = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-v3')
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+# UI dengan Streamlit
+st.title("🔍 Perbandingan Bi-Encoder vs Cross-Encoder")
+st.subheader("Masukkan Query dan Passages untuk melihat perbandingan ranking")
+
+# Input untuk query dan passages
+query = st.text_input("Masukkan Query:", "Apa manfaat AI dalam kehidupan sehari-hari?")
+passages = st.text_area("Masukkan Passages (pisahkan dengan enter):", 
+                        "AI membantu mengoptimalkan pencarian informasi dan otomatisasi tugas.\n"
+                        "Teknologi AI telah berkembang pesat dalam bidang kesehatan dan pendidikan.\n"
+                        "AI digunakan untuk meningkatkan efisiensi industri seperti manufaktur dan e-commerce.")
+
+# Konversi input passages ke list
+passages = passages.split("\n")
+
+if st.button("Jalankan Model"):
+    st.write("🚀 **Menganalisis passages dengan dua model...**")
+
+    # --- Bi-Encoder ---
+    start_time = time.time()
+    query_emb = bi_encoder.encode(query)
+    passage_embs = bi_encoder.encode(passages)
+    scores_bi = [torch.cosine_similarity(torch.tensor(query_emb), torch.tensor(p_emb), dim=0).item() for p_emb in passage_embs]
+    bi_time = time.time() - start_time  # Waktu eksekusi Bi-Encoder
+
+    # --- Cross-Encoder ---
+    start_time = time.time()
+    scores_cross = cross_encoder.predict([[query, passage] for passage in passages])
+    cross_time = time.time() - start_time  # Waktu eksekusi Cross-Encoder
+
+    # Hitung Mean Reciprocal Rank (MRR)
+    def compute_mrr(scores):
+        ranked_scores = sorted(scores, reverse=True)
+        if len(ranked_scores) == 0:
+            return 0.0
+        return 1 / (ranked_scores.index(max(scores)) + 1)
+
+    mrr_bi = compute_mrr(scores_bi)
+    mrr_cross = compute_mrr(scores_cross)
+
+    # **Tampilkan Hasil**
+    st.write("✅ **Hasil Ranking (Bi-Encoder)**")
+    sorted_bi = sorted(zip(passages, scores_bi), key=lambda x: x[1], reverse=True)
+    for i, (text, score) in enumerate(sorted_bi):
+        st.write(f"{i+1}. **[{score:.4f}]** {text}")
+
+    st.write("✅ **Hasil Ranking (Cross-Encoder)**")
+    sorted_cross = sorted(zip(passages, scores_cross), key=lambda x: x[1], reverse=True)
+    for i, (text, score) in enumerate(sorted_cross):
+        st.write(f"{i+1}. **[{score:.4f}]** {text}")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # **Tampilkan metrik**
+    st.subheader("📊 Perbandingan Model")
+    st.write(f"⏱ **Waktu Eksekusi Bi-Encoder:** {bi_time:.3f} detik")
+    st.write(f"⏱ **Waktu Eksekusi Cross-Encoder:** {cross_time:.3f} detik")
+    st.write(f"📈 **MRR Bi-Encoder:** {mrr_bi:.3f}")
+    st.write(f"📈 **MRR Cross-Encoder:** {mrr_cross:.3f}")