Create app.py

app.py

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+from flask import Flask, render_template, request
+from datasets import load_dataset
+import torch
+from transformers import AutoTokenizer, AutoModel
+import numpy as np
+
+# --- 1. Initialize Flask App ---
+app = Flask(__name__)
+
+# --- 2. Load Models and Dataset (Done once on startup) ---
+print("Loading models and dataset...")
+# Point this to your Hugging Face Dataset repository
+DATASET_REPO = "YourUsername/bible-rag-gemma-with-faiss" 
+MODEL_NAME = "google/embeddinggemma-300m"
+
+# Load the pre-built dataset and FAISS index
+rag_dataset = load_dataset(DATASET_REPO)['train']
+
+# Load the Gemma model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+embedding_model = AutoModel.from_pretrained(MODEL_NAME)
+print("Models and dataset loaded successfully!")
+
+# --- 3. Define App Routes ---
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/search', methods=['POST'])
+def search():
+    user_query = request.form['query']
+    if not user_query:
+        return render_template('index.html', results=[])
+
+    # --- Create embedding for the user's query ---
+    inputs = tokenizer(user_query, return_tensors="pt")
+    with torch.no_grad():
+        outputs = embedding_model(**inputs)
+    query_embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy()
+    
+    # FAISS expects a flattened numpy array
+    query_embedding = np.float32(query_embedding)
+
+    # --- Search the FAISS index ---
+    scores, retrieved_examples = rag_dataset.get_nearest_examples(
+        'embeddings',
+        query_embedding,
+        k=10 # Get top 10 results
+    )
+
+    # --- Format results for display ---
+    results_list = []
+    for i in range(len(scores)):
+        results_list.append({
+            'score': scores[i],
+            'text': retrieved_examples['text'][i],
+            'reference': retrieved_examples['reference'][i],
+            'version': retrieved_examples['version'][i]
+        })
+    
+    return render_template('index.html', results=results_list, query=user_query)
+
+# --- 4. Run the App ---
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)