Delete intent.py

intent.py

@@ -1,53 +0,0 @@
-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))