Create intent.py

intent.py

@@ -0,0 +1,53 @@
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load pre-trained model and tokenizer from Hugging Face
+MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"  # Example, change to other open-source models if necessary
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=2)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Define the intents
+intents = {0: "database_query", 1: "product_description"}
+
+# Function to classify query intent
+def classify_intent(query):
+    inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probabilities).item()
+    return intents[predicted_class], probabilities[0][predicted_class].item()
+
+# Example usage
+query_1 = "Fetch all products with the keyword 'T-shirt' from the database."
+query_2 = "Can you tell me about the description of this Shopify store?"
+
+intent_1, confidence_1 = classify_intent(query_1)
+intent_2, confidence_2 = classify_intent(query_2)
+
+print(f"Query 1: '{query_1}'\nIntent: {intent_1} with confidence {confidence_1}\n")
+print(f"Query 2: '{query_2}'\nIntent: {intent_2} with confidence {confidence_2}\n")
+
+# Further routing based on classified intent
+def handle_query(query):
+    intent, confidence = classify_intent(query)
+    if intent == "database_query":
+        # Call the natural language to SQL engine
+        return execute_database_query(query)
+    elif intent == "product_description":
+        # Call the RAG engine for product descriptionß
+        return execute_rag_query(query)
+    else:
+        return "Intent not recognized."
+
+# Placeholder functions for database and RAG query handling
+def execute_database_query(query):
+    # Integrate with SQL-based natural language query generator
+    return "Executing database query..."
+
+def execute_rag_query(query):
+    # Integrate with RAG pipeline to retrieve product descriptions
+    return "Executing RAG product description query..."
+
+# Test the function with different queries
+print(handle_query(query_1))
+print(handle_query(query_2))