Create app.py

Framework build 1

app.py

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+import gradio as gr
+from gliner import GLiNER
+from vllm import LLM, SamplingParams
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import json
+import torch
+
+# Load mock legal corpus
+with open("legal_corpus.json", "r", encoding="utf-8") as f:
+    corpus = json.load(f)
+documents = [item["text"] for item in corpus]
+
+# Initialize sentence transformer for embeddings
+embedder = SentenceTransformer("all-MiniLM-L6-v2")  # Lightweight embedder
+embeddings = embedder.encode(documents, convert_to_numpy=True)
+
+# Initialize FAISS index
+dimension = embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(embeddings)
+
+# Initialize GLiNER model
+gliner_model = GLiNER.from_pretrained("NAMAA-Space/gliner_arabic-v2.1", load_tokenizer=True)
+
+# Initialize QwQ-32B
+llm = LLM(
+    model="Qwen/QwQ-32B",
+    quantization="awq",
+    max_model_len=4096,
+    gpu_memory_utilization=0.9
+)
+sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
+
+def retrieve_documents(query, k=2):
+    """Retrieve top-k relevant documents using FAISS."""
+    query_embedding = embedder.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_embedding, k)
+    return [documents[idx] for idx in indices[0]]
+
+def run_ner(text, entity_types):
+    """Run NER with gliner_arabic-v2.1."""
+    if not text or not entity_types:
+        return []
+    entity_list = [e.strip() for e in entity_types.split(",")]
+    entities = gliner_model.predict_entities(text, entity_list, threshold=0.5)
+    return [{"text": e["text"], "label": e["label"], "score": round(e["score"], 2)} for e in entities]
+
+def generate_legal_insight(text, entities, retrieved_docs):
+    """Generate insight with QwQ-32B using RAG."""
+    entity_str = ", ".join([f"{e['text']} ({e['label']})" for e in entities])
+    context = "\n".join(retrieved_docs)
+    prompt = f"""You are a legal assistant for Arabic law. Using the following context and extracted entities, provide a concise legal insight (e.g., summary or explanation). Ensure the response is grounded in the context and entities.
+
+Context:
+{context}
+
+Entities:
+{entity_str}
+
+Input Text:
+{text}
+
+Insight:"""
+    outputs = llm.generate([prompt], sampling_params)
+    return outputs[0].outputs[0].text
+
+def main_interface(text, entity_types):
+    """Main Gradio interface."""
+    # Run NER
+    ner_result = run_ner(text, entity_types)
+    
+    # Retrieve relevant documents
+    retrieved_docs = retrieve_documents(text)
+    
+    # Generate legal insight
+    insight = generate_legal_insight(text, ner_result, retrieved_docs)
+    
+    return ner_result, retrieved_docs, insight
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("# Arabic Legal Demo: NER & RAG with GLiNER and QwQ-32B")
+    with gr.Row():
+        text_input = gr.Textbox(label="Arabic Legal Text", lines=5, placeholder="Enter Arabic legal text...")
+        entity_types = gr.Textbox(
+            label="Entity Types (comma-separated)",
+            value="person,law,organization",
+            placeholder="e.g., person,law,organization"
+        )
+    submit_btn = gr.Button("Analyze")
+    ner_output = gr.JSON(label="Extracted Entities")
+    docs_output = gr.Textbox(label="Retrieved Legal Context")
+    insight_output = gr.Textbox(label="Legal Insight")
+    
+    submit_btn.click(
+        fn=main_interface,
+        inputs=[text_input, entity_types],
+        outputs=[ner_output, docs_output, insight_output]
+    )
+
+demo.launch()