Update app.py

app.py

@@ -1,12 +1,51 @@
 import gradio as gr
+from sql_generator import SQLGenerator
+from intent_classifier import IntentClassifier
+from rag_system import RAGSystem
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# 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]],
@@ -39,26 +78,47 @@ def respond(
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-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)",
+# 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__":
-    demo.launch()
+    create_interface()