Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from wikipediaapi import Wikipedia
+import textwrap
+import numpy as np
+import openai
+
+# Function to process the input and generate the output
+def process_query(wiki_page, model_name, embed_dim, query, api_key):
+    model_mapping = {
+        "Arabic-mpnet-base-all-nli-triplet": "Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet",
+        "Arabic-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka",
+        "Arabert-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka",
+        "Arabic-labse-Matryoshka": "Omartificial-Intelligence-Space/Arabic-labse-Matryoshka",
+        "Marbert-all-nli-triplet-Matryoshka": "Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka"
+    }
+
+    model_path = model_mapping[model_name]
+    model = SentenceTransformer(model_path, trust_remote_code=True, truncate_dim=embed_dim)
+    wiki = Wikipedia('RAGBot/0.0', 'ar')
+    doc = wiki.page(wiki_page).text
+    paragraphs = doc.split('\n\n')  # chunking
+
+    for i, p in enumerate(paragraphs):
+        wrapped_text = textwrap.fill(p, width=100)
+
+    docs_embed = model.encode(paragraphs, normalize_embeddings=True)
+    query_embed = model.encode(query, normalize_embeddings=True)
+    similarities = np.dot(docs_embed, query_embed.T)
+    top_3_idx = np.argsort(similarities, axis=0)[-3:][::-1].tolist()
+    most_similar_documents = [paragraphs[idx] for idx in top_3_idx]
+
+    CONTEXT = ""
+    for i, p in enumerate(most_similar_documents):
+        wrapped_text = textwrap.fill(p, width=100)
+        CONTEXT += wrapped_text + "\n\n"
+
+    prompt = f"""
+        use the following CONTEXT to answer the QUESTION at the end.
+        If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+        CONTEXT: {CONTEXT}
+        QUESTION: {query}
+    """
+
+    client = openai.OpenAI(api_key=api_key)
+    response = client.chat_completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "user", "content": prompt},
+        ]
+    )
+
+    return response.choices[0].message.content
+
+# Define the interface
+wiki_page_input = gr.Textbox(label="Wikipedia Page (in Arabic)")
+query_input = gr.Textbox(label="Query (in Arabic)")
+api_key_input = gr.Textbox(label="OpenAI API Key", type="password")
+
+model_choice = gr.Dropdown(
+    choices=[
+        "Arabic-mpnet-base-all-nli-triplet",
+        "Arabic-all-nli-triplet-Matryoshka",
+        "Arabert-all-nli-triplet-Matryoshka",
+        "Arabic-labse-Matryoshka",
+        "Marbert-all-nli-triplet-Matryoshka"
+    ], 
+    label="Choose Embedding Model"
+)
+
+embed_dim_choice = gr.Dropdown(
+    choices=[768, 512, 256, 128, 64],
+    label="Embedding Dimension"
+)
+
+output_text = gr.Textbox(label="Output")
+
+gr.Interface(
+    fn=process_query,
+    inputs=[wiki_page_input, model_choice, embed_dim_choice, query_input, api_key_input],
+    outputs=output_text,
+    title="Arabic Wiki RAG",
+    description="Choose a Wikipedia page, embedding model, and dimension to answer a query in Arabic."
+).launch()