Create app.py

app.py

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+from dotenv import load_dotenv
+import gradio as gr
+import cohere
+from typing import Dict, List, Optional
+import json
+from dataclasses import dataclass
+import os
+from datetime import datetime
+
+# Load environment variables
+load_dotenv()
+
+
+@dataclass
+class IntentResponse:
+    intent: str
+    confidence: float
+    entities: Dict
+    suggested_action: str
+    explanation: str
+
+
+class EcommerceLLMIntentRecognizer:
+    def __init__(self):
+        # Get API key from environment variable
+        api_key = os.getenv('COHERE_API_KEY')
+        if not api_key:
+            raise ValueError("Please set COHERE_API_KEY environment variable")
+
+        self.co = cohere.Client(api_key)
+
+        # Define our intent taxonomy
+        self.valid_intents = {
+            'product_search': 'SEARCH_CATALOG',
+            'price_inquiry': 'FETCH_PRICE',
+            'order_status': 'CHECK_ORDER_STATUS',
+            'return_request': 'INITIATE_RETURN',
+            'cart_management': 'MODIFY_CART',
+            'availability_check': 'CHECK_INVENTORY',
+            'checkout_help': 'ASSIST_CHECKOUT',
+            'shipping_info': 'PROVIDE_SHIPPING_INFO',
+            'product_comparison': 'COMPARE_PRODUCTS',
+            'size_guide': 'SHOW_SIZE_GUIDE',
+            'warranty_info': 'PROVIDE_WARRANTY_INFO',
+            'cancel_order': 'PROCESS_CANCELLATION'
+        }
+
+    def _generate_prompt(self, query: str) -> str:
+        return f"""As an e-commerce AI assistant, analyze the following customer query and extract the shopping intent, relevant entities, and determine the appropriate action.
+
+Valid intents are: {', '.join(self.valid_intents.keys())}
+
+Customer Query: "{query}"
+
+Provide your analysis in the following JSON format:
+{{
+    "intent": "the_identified_intent",
+    "confidence": 0.XX,
+    "entities": {{
+        "product": "identified_product",
+        "category": "product_category",
+        "specifications": ["any", "relevant", "specs"],
+        "quantity": "if_mentioned",
+        "order_id": "if_mentioned",
+        "price_range": {{
+            "min": "if_mentioned",
+            "max": "if_mentioned"
+        }}
+    }},
+    "explanation": "Brief explanation of why this intent was chosen"
+}}
+
+JSON Response:"""
+
+    def recognize_intent(self, query: str) -> IntentResponse:
+        # Generate LLM response
+        response = self.co.generate(
+            model='command',
+            prompt=self._generate_prompt(query),
+            max_tokens=500,
+            temperature=0.2,
+            k=0,
+            stop_sequences=["\n\n"],
+            return_likelihoods='NONE'
+        )
+
+        try:
+            # Parse the LLM's response
+            result = json.loads(response.generations[0].text)
+
+            # Map to our action system
+            suggested_action = self.valid_intents.get(
+                result['intent'],
+                'UNKNOWN_ACTION'
+            )
+
+            return IntentResponse(
+                intent=result['intent'],
+                confidence=result['confidence'],
+                entities=result['entities'],
+                suggested_action=suggested_action,
+                explanation=result['explanation']
+            )
+
+        except json.JSONDecodeError:
+            return IntentResponse(
+                intent='parse_error',
+                confidence=0.0,
+                entities={},
+                suggested_action='HANDLE_ERROR',
+                explanation='Failed to parse LLM response'
+            )
+
+
+def process_query(user_query: str) -> str:
+    try:
+        recognizer = EcommerceLLMIntentRecognizer()
+        response = recognizer.recognize_intent(user_query)
+
+        return json.dumps({
+            'timestamp': datetime.now().isoformat(),
+            'query': user_query,
+            'intent': response.intent,
+            'confidence': response.confidence,
+            'entities': response.entities,
+            'suggested_action': response.suggested_action,
+            'explanation': response.explanation
+        }, indent=2)
+    except ValueError as e:
+        return json.dumps({
+            'error': str(e),
+            'hint': 'Please ensure COHERE_API_KEY is set in your .env file'
+        }, indent=2)
+
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_query,
+    inputs=gr.Textbox(label="Enter customer query"),
+    outputs=gr.JSON(label="Intent Analysis"),
+    title="E-commerce LLM Intent Recognition System",
+    description="""This system uses Cohere's Command model to understand customer intentions in an e-commerce context.
+                  Enter your query to see the detailed intent analysis.""",
+    examples=[
+        ["I'm looking for a waterproof smart watch under $300"],
+        ["Can you compare the iPhone 13 and iPhone 14 Pro?"],
+        ["Need to return my order #ABC123, it's the wrong size"],
+        ["Do you have this dress in size medium and in red?"],
+        ["What's your shipping time to California?"]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()