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app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+import pandas as pd
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+# --- Configuration ---
+MODEL_NAME = "Qwen/Qwen3-Embedding-0.6B"  # Placeholder for your model
+KURAL_EMBEDDINGS_FILE = "kural_embeds.pt"
+KURAL_DATA_FILE = "thirukural.tsv" # You'll need a CSV with the Kural text
+
+# --- Load Resources ---
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
+except Exception as e:
+    print(f"Error loading Transformer model: {e}")
+    # Handle model loading failure (e.g., exit or use a fallback)
+
+try:
+    kural_embeddings = torch.load(KURAL_EMBEDDINGS_FILE)
+except FileNotFoundError:
+    print(f"Error: The file {KURAL_EMBEDDINGS_FILE} was not found.")
+
+try:
+    df = pd.read_csv(KURAL_DATA_FILE, sep='\t')
+except FileNotFoundError:
+    print(f"Error: The file {KURAL_DATA_FILE} was not found.")
+
+def get_detailed_instruct(query: str) -> str:
+    return f'Instruct: Given a question, retrieve relevant Thirukkural couplets that are most relevant to, or answer the question\nQuery:{query}'
+    
+def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
+    '''
+    Returns pooled embedding of last token from Qwen3
+    '''
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+def find_relevant_kurals(question):
+    """
+    Finds the top 5 most relevant Kurals using cosine similarity.
+    """
+    batch_dict = tokenizer([get_detailed_instruct(question)], max_length=128, padding=False, truncation=True, return_tensors='pt')
+    outputs = model(**batch_dict)
+    query_embedding = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']).detach().cpu()
+    
+    # Calculate similarities
+    all_embeddings = torch.cat((query_embedding,kural_embeddings), axis=0)
+    all_embeddings = F.normalize(all_embeddings, p=2, dim=1)
+    scores = all_embeddings[:1]@all_embeddings[1:].T
+
+    
+    # Get top 5 indices
+    top_indices = torch.topk(scores[0,:], 3).indices.tolist()
+    
+    # Prepare results
+    results = []
+    for i in top_indices:
+        results.append({
+            "kural_ta": df.iloc[i].get("kural", "N/A"),
+            "kural_eng": df.iloc[i].get("kural_eng", "N/A"),
+            "chapter": df.iloc[i].get("chapter", "N/A"),
+            "similarity": scores[0,i]
+        })
+    return results
+
+def rag_interface(question):
+    """
+    The main function for the Gradio interface.
+    """
+    if not question:
+        return "Please enter a question."
+        
+    kurals = find_relevant_kurals(question)
+    
+    output = ""
+    for kural in kurals:
+        output += f"**Kural (Tamil):** {kural['kural_ta']}<br>"
+        output += f"**Kural (English):** {kural['kural_eng']}<br>"
+        output += f"**Chapter:** {kural['chapter']}<br>"
+        output += f"**Similarity:** {kural['similarity']:.2f}\n\n---\n"
+        
+    return output
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=rag_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your question here:"),
+    outputs="markdown",
+    title="Kural for your question.",
+    description="Ask a vexing question and get 3 relevant Thirukural couplets using embedding-similarity based search.",
+    flagging_mode='never'
+)
+
+if __name__ == "__main__":
+    iface.launch()

kural_embeds.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe85848bea6c0971803d987dccec28bff72e534fc4cf4f95558b20218b17e7c2
+size 5448885

requirements.txt

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+gradio
+transformers
+pandas
+numpy
+torch