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| import gradio as gr | |
| from sql_generator import SQLGenerator | |
| from intent_classifier import IntentClassifier | |
| from rag_system import RAGSystem | |
| from huggingface_hub import InferenceClient | |
| # Initialize Hugging Face InferenceClient | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Unified System Class | |
| class UnifiedSystem: | |
| def __init__(self): | |
| self.sql_generator = SQLGenerator() | |
| self.intent_classifier = IntentClassifier() | |
| self.rag_system = RAGSystem() | |
| self.base_url = "https://agkd0n-fa.myshopify.com/products/" | |
| def process_query(self, query): | |
| intent, confidence = self.intent_classifier.classify(query) | |
| if intent == "database_query": | |
| sql_query = self.sql_generator.generate_query(query) | |
| products = self.sql_generator.fetch_shopify_data("products") | |
| if products and 'products' in products: | |
| results = "\n".join([ | |
| f"Title: {p['title']}\nVendor: {p['vendor']}\nDescription: {p.get('body_html', 'No description available.')}\nURL: {self.base_url}{p['handle']}\n" | |
| for p in products['products'] | |
| ]) | |
| return f"Intent: Database Query (Confidence: {confidence:.2f})\n\n" \ | |
| f"SQL Query: {sql_query}\n\nResults:\n{results}" | |
| else: | |
| return "No results found or error fetching data from Shopify." | |
| elif intent == "product_description": | |
| rag_response = self.rag_system.process_query(query) | |
| product_handles = rag_response.get('product_handles', []) | |
| urls = [f"{self.base_url}{handle}" for handle in product_handles] | |
| response = rag_response.get('response', "No description available.") | |
| return f"Intent: Product Description (Confidence: {confidence:.2f})\n\n" \ | |
| f"Response: {response}\n\nProduct Details:\n" + "\n".join( | |
| [f"Product URL: {url}" for url in urls] | |
| ) | |
| return "Intent not recognized." | |
| # Chatbot Response using Hugging Face's model | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| # Create Gradio interface with integrated functionalities | |
| def create_interface(): | |
| system = UnifiedSystem() | |
| # Define the interface | |
| iface = gr.Interface( | |
| fn=system.process_query, | |
| inputs=gr.Textbox( | |
| label="Enter your query", | |
| placeholder="e.g., 'Show me all T-shirts' or 'Describe the product features'" | |
| ), | |
| outputs=gr.Textbox(label="Response"), | |
| title="Unified Query Processing System", | |
| description="Enter a natural language query to search products or get descriptions.", | |
| examples=[ | |
| ["Show me shirts less than 50 rupee"], | |
| ["Show me shirts with red color"], | |
| ["Show me T-shirts with M size"] | |
| ] | |
| ) | |
| # Define Chat Interface for Hugging Face Model | |
| chat_demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| # Launch both interfaces (Unified System and Chatbot) | |
| iface.launch(share=True) # Share the interface for public access | |
| chat_demo.launch(share=True) # Launch the chatbot interface for user interaction | |
| if __name__ == "__main__": | |
| create_interface() | |