Create app.py

app.py

@@ -0,0 +1,152 @@
+import torch
+import gradio as gr
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# -----------------------------
+# LOAD SEMANTIC DATASET
+# -----------------------------
+DATASET_ID = "Talip7/scikit-learn-issues-embeddings-mpnet"
+
+train_ds = load_dataset(DATASET_ID, split="train")
+train_ds = train_ds.add_faiss_index(column="embedding")
+
+# -----------------------------
+# LOAD EMBEDDING MODEL
+# -----------------------------
+EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
+
+encoder = SentenceTransformer(
+    EMBEDDING_MODEL,
+    device="cuda" if torch.cuda.is_available() else "cpu"
+)
+
+# -----------------------------
+# LOAD MULTILABEL CLASSIFIER
+# -----------------------------
+CLASSIFIER_ID = "Talip7/scikit-learn-multilabel-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(CLASSIFIER_ID)
+clf_model = AutoModelForSequenceClassification.from_pretrained(
+    CLASSIFIER_ID,
+    problem_type="multi_label_classification"
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+clf_model.eval()
+
+# -----------------------------
+# UTILS
+# -----------------------------
+def predict_labels(text, threshold=0.5):
+    inputs = tokenizer(
+        text,
+        truncation=True,
+        padding=True,
+        max_length=512,
+        return_tensors="pt"
+    )
+    inputs = {k: v.to(clf_model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        logits = clf_model(**inputs).logits
+
+    probs = torch.sigmoid(logits)[0].cpu().numpy()
+
+    labels = []
+    for i, p in enumerate(probs):
+        if p >= threshold:
+            labels.append(clf_model.config.id2label[i])
+
+    return labels
+
+
+def semantic_search(query, k=10):
+    query_emb = encoder.encode(query, convert_to_numpy=True)
+    scores, samples = train_ds.get_nearest_examples(
+        "embedding",
+        query_emb,
+        k=k
+    )
+    return scores, samples
+
+
+def hybrid_search(query, alpha=0.7, beta=0.3, max_results=5):
+    sem_scores, sem_results = semantic_search(query, k=15)
+    predicted_labels = set(predict_labels(query))
+
+    seen = set()
+    results = []
+
+    for i in range(len(sem_scores)):
+        issue_id = sem_results["issue_number"][i]
+        if issue_id in seen:
+            continue
+        seen.add(issue_id)
+
+        issue_labels = set(sem_results["labels"][i])
+        overlap = (
+            len(issue_labels & predicted_labels) / len(issue_labels)
+            if issue_labels else 0.0
+        )
+
+        final_score = alpha * float(sem_scores[i]) + beta * overlap
+
+        results.append({
+            "Issue": f"#{issue_id}",
+            "Final score": round(final_score, 3),
+            "Semantic": round(float(sem_scores[i]), 3),
+            "Label overlap": round(overlap, 2),
+            "Labels": ", ".join(issue_labels),
+            "URL": sem_results["html_url"][i],
+        })
+
+        if len(results) >= max_results:
+            break
+
+    return predicted_labels, results
+
+
+# -----------------------------
+# GRADIO UI
+# -----------------------------
+def run_search(query):
+    if not query.strip():
+        return "Please enter a query.", []
+
+    labels, results = hybrid_search(query)
+
+    label_text = ", ".join(labels) if labels else "No label confidently predicted"
+    return label_text, results
+
+
+with gr.Blocks(title="GitHub Issue Hybrid Search") as demo:
+    gr.Markdown(
+        """
+        # 🐙 GitHub Issue Hybrid Search & Auto-Label Assistant
+
+        **Semantic Search (MPNet) + Multilabel Classification (DistilBERT)**  
+        Precision-first hybrid ranking on real scikit-learn issues.
+        """
+    )
+
+    query = gr.Textbox(
+        label="Describe the issue",
+        placeholder="e.g. RandomForestClassifier crashes when sample_weight is None"
+    )
+
+    btn = gr.Button("Search")
+
+    predicted = gr.Textbox(label="Predicted labels")
+    table = gr.Dataframe(
+        headers=["Issue", "Final score", "Semantic", "Label overlap", "Labels", "URL"],
+        wrap=True
+    )
+
+    btn.click(
+        fn=run_search,
+        inputs=query,
+        outputs=[predicted, table]
+    )
+
+demo.launch()