app.py Besh 2 (Push 8)

app.py

@@ -6,6 +6,7 @@ from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 import spacy
 import os
 import logging
+import re
 
 # Set up logging with detailed output
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -38,8 +39,8 @@ else:
 tokenizer = BlenderbotTokenizer.from_pretrained(model_dir)
 model = BlenderbotForConditionalGeneration.from_pretrained(model_dir)
 
-# Static Context
-context_msg = "Hutter Products GmbH provides sustainable products like shirts and shorts..."
+# No Static Context
+context_msg = ""
 
 # spaCy Setup
 spacy_model_path = "/home/user/app/en_core_web_sm-3.8.0"
@@ -63,20 +64,23 @@ def extract_keywords(text: str) -> List[str]:
 
 def detect_intent(text: str) -> str:
     doc = nlp(text.lower())
-    # Stricter matching for "shirt" vs "short"
-    if "shirt" in [token.text for token in doc]:  # Exact match for "shirt"
+    text_lower = text.lower()
+    if "shirt" in [token.text for token in doc]:
         return "recommend_shirt"
-    elif "short" in [token.text for token in doc]:  # Exact match for "short"
+    elif "short" in [token.text for token in doc]:
         return "recommend_shorts"
     elif any(token.text in ["what", "who", "company", "do", "products"] for token in doc):
         return "company_info"
+    elif "name" in text_lower:
+        return "ask_name"
+    elif re.search(r"\d+\s*[\+\-\*/]\s*\d+", text_lower):
+        return "math_query"
     return "unknown"
 
 def search_products_by_keywords(keywords: List[str]) -> List[Dict[str, Any]]:
     if not keywords:
         logger.info("No keywords provided, returning empty product list.")
         return []
-    # Use stricter matching: only return products with exact keyword in name
     query = {"$or": [{"name": {"$regex": f"\\b{keyword}\\b", "$options": "i"}} for keyword in keywords]}
     matched_products = [
         {
@@ -96,12 +100,31 @@ def get_product_context(products: List[Dict]) -> str:
     product_str += ", ".join([f"'{p['name']}' - {p['description']}" for p in products[:2]])
     return product_str
 
-def format_response(response: str, products: List[Dict], intent: str) -> str:
-    if intent in ["recommend_shirt", "recommend_shorts"] and products:
-        product = products[0]
-        return f"{response} For example, check out our '{product['name']}'—it’s {product['description'].lower()}!"
+def format_response(response: str, products: List[Dict], intent: str, input_text: str) -> str:
+    if intent == "recommend_shirt" or intent == "recommend_shorts":
+        if products:
+            product = products[0]
+            return f"{response} For example, check out our '{product['name']}'—it’s {product['description'].lower()}!"
+        return response
     elif intent == "company_info":
         return f"{response} At Hutter Products GmbH, we specialize in sustainable product design and production!"
+    elif intent == "ask_name":
+        return "I’m Grok, your friendly assistant from Hutter Products GmbH. How can I help you today?"
+    elif intent == "math_query":
+        match = re.search(r"(\d+)\s*([\+\-\*/])\s*(\d+)", input_text.lower())
+        if match:
+            num1, op, num2 = int(match.group(1)), match.group(2), int(match.group(3))
+            if op == "+":
+                return f"{num1} plus {num2} is {num1 + num2}!"
+            elif op == "-":
+                return f"{num1} minus {num2} is {num1 - num2}!"
+            elif op == "*":
+                return f"{num1} times {num2} is {num1 * num2}!"
+            elif op == "/":
+                return f"{num1} divided by {num2} is {num1 / num2}!" if num2 != 0 else "Can’t divide by zero!"
+        return "I can do simple math! Try something like '1 + 1'."
+    elif intent == "unknown":
+        return response  # Let BlenderBot respond freely for unknown intent
     return response
 
 # Endpoints
@@ -125,7 +148,7 @@ async def process_prompt(request: PromptRequest):
         logger.info(f"Products matched: {len(products)}")
 
         history_str = " || ".join(history)
-        full_input = f"{history_str} || {product_context} {context_msg} || {input_text}" if history else f"{product_context} {context_msg} || {input_text}"
+        full_input = f"{history_str} || {product_context} || {input_text}" if (history or product_context) else input_text
         logger.info(f"Full input to model: {full_input}")
 
         logger.info("Tokenizing input...")
@@ -140,7 +163,7 @@ async def process_prompt(request: PromptRequest):
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
         logger.info(f"Model response: {response}")
 
-        enhanced_response = format_response(response, products, intent)
+        enhanced_response = format_response(response, products, intent, input_text)
         qa_response = {
             "question": input_text,
             "answer": enhanced_response,