Update electronics_query.py

electronics_query.py

@@ -1,7 +1,5 @@
-import os
-import json
 import pandas as pd
-from termcolor import colored
+import random
 
 # Load the electronics dataset
 def load_electronics_dataset():
@@ -11,84 +9,59 @@ def load_electronics_dataset():
     except Exception as e:
         raise Exception(f"Error loading electronics dataset: {e}")
 
-# Function to query the dataset
-def query_electronics(query, n_results=5):
-    electronics_df = load_electronics_dataset()
-    
-    # Apply filters to the dataset based on query
-    filtered_df = electronics_df[electronics_df.apply(lambda row: query.lower() in row['ProductName'].lower(), axis=1)]
-    
-    # Sort by relevance (example: based on some column like Rating or Price)
-    sorted_df = filtered_df.sort_values(by='Rating', ascending=False)
-    
-    # Get top n_results
-    top_results = sorted_df.head(n_results)
-    
-    return top_results
+# Define different response styles
+electronics_response_templates = [
+    lambda row: (
+        f"The {row['ProductName']} is a great choice if you're looking for a {row['Category']}. "
+        f"Priced at ${row['Price']}, customers appreciate its {row['Description']}. "
+        f"Would you like to know more about the {row['ProductName']}?"
+    ),
+    lambda row: (
+        f"Check out the {row['ProductName']}! It's available for ${row['Price']} and is known for its {row['Description']}. "
+        f"What do you think about this {row['Category']}?"
+    ),
+    # Add more templates as needed
+]
 
-# Function to save query results to a JSON file
-def save_results_to_json(results, query, n_results):
-    # Create a dictionary to store the results
-    json_results = {
-        "query": query,
-        "n_results": n_results,
-        "results": []
-    }
-    
-    # Iterate through the results and add them to the dictionary
-    for i, row in results.iterrows():
-        json_results["results"].append({
-            "result_number": i + 1,
-            "product_name": row['ProductName'],
-            "category": row['Category'],
-            "price": row['Price'],
-            "rating": row['Rating'],
-            "description": row['Description']
-        })
-    
-    # Write the results to a JSON file
-    with open("electronics_query_results.json", "w") as f:
-        json.dump(json_results, f, indent=4)
+# Generate a dynamic response
+def generate_electronics_response(row):
+    template = random.choice(electronics_response_templates)
+    return template(row)
+
+# Extract filters from the query
+def extract_electronics_filters(query):
+    filters = {}
+    query_lower = query.lower()
+
+    if 'best' in query_lower and 'rating' in query_lower:
+        filters['Rating'] = 'max'
 
-# Main function to run the query system
-def main():
-    # Print welcome message
-    print(colored("Welcome to the Electronics Query System!", "green"))
-    print("You can ask questions about the electronics in the dataset.")
+    if 'phones' in query_lower:
+        filters['Category'] = 'phone'
+    elif 'laptops' in query_lower:
+        filters['Category'] = 'laptop'
     
-    # Main loop for user interaction
-    while True:
-        # Get user query
-        query = input(colored("\nEnter your query (or 'quit' to exit): ", "yellow"))
-        
-        # Check if user wants to quit
-        if query.lower() == 'quit':
-            break
-        
-        # Get number of desired results
-        n_results = int(input(colored("How many results would you like? ", "yellow")))
-        
-        # Query the dataset
-        results = query_electronics(query, n_results)
-        
-        # Print the results
-        print(colored("\nTop matching products:", "green"))
-        for i, row in results.iterrows():
-            print(colored(f"\nResult {i + 1}:", "blue"))
-            print(f"Product: {row['ProductName']}")
-            print(f"Category: {row['Category']}")
-            print(f"Price: ${row['Price']}")
-            print(f"Rating: {row['Rating']}")
-            print(colored("Description:", "cyan"))
-            print(row['Description'])
-        
-        # Save results to JSON file
-        save_results_to_json(results, query, n_results)
-        print(colored("\nResults have been saved to electronics_query_results.json", "green"))
+    return filters
 
-    # Print goodbye message
-    print(colored("Thank you for using the Electronics Query System!", "green"))
+# Apply filters to the electronics DataFrame
+def apply_electronics_filters(df, filters):
+    for key, value in filters.items():
+        if key == 'Rating' and value == 'max':
+            df = df[df['Rating'] == df['Rating'].max()]
+        elif key in df.columns and isinstance(value, str):
+            df = df[df[key].str.contains(value, case=False, na=False)]
+    return df
+
+# Query electronics based on user input
+def query_electronics(user_query, n_results=5):
+    electronics_df = load_electronics_dataset()
+    filtered_df = apply_electronics_filters(electronics_df, extract_electronics_filters(user_query))
+    
+    # Check if 'Rating' column exists before sorting
+    if 'Rating' in filtered_df.columns:
+        sorted_df = filtered_df.sort_values(by='Rating', ascending=False)
+    else:
+        sorted_df = filtered_df  # Skip sorting if 'Rating' is not available
 
-# Check if the script is being run directly
-if __name__ == "__main__":
-    main()
+    # Return the top N results
+    return sorted_df.head(n_results)