Updated with Content msg, product recommendation and history information.

app.py

@@ -39,8 +39,8 @@ else:
 tokenizer = BlenderbotTokenizer.from_pretrained(model_dir)
 model = BlenderbotForConditionalGeneration.from_pretrained(model_dir)
 
-# No Static Context
-context_msg = ""
+# Static Context based on Hutter Products GmbH home page
+context_msg = "I am Hutter, your shopping guide for Hutter Products GmbH. I’m here to help you explore our innovative and sustainable product catalog, featuring eco-friendly items like recycled textiles and ocean plastic goods. Let me assist you in finding the perfect sustainable solution!"
 
 # spaCy Setup
 spacy_model_path = "/home/user/app/en_core_web_sm-3.8.0"
@@ -65,17 +65,15 @@ def extract_keywords(text: str) -> List[str]:
 def detect_intent(text: str) -> str:
     doc = nlp(text.lower())
     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]:
-        return "recommend_shorts"
-    elif any(token.text in ["what", "who", "company", "do", "products"] for token in doc):
+    if any(token.text in ["buy", "shop", "find", "recommend", "product", "products", "item", "textile", "jacket", "shirt", "shorts"] for token in doc):
+        return "recommend_product"
+    elif any(token.text in ["what", "who", "company", "do"] 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"
+    return "recommend_product"  # Default to product exploration
 
 def search_products_by_keywords(keywords: List[str]) -> List[Dict[str, Any]]:
     if not keywords:
@@ -101,31 +99,33 @@ def get_product_context(products: List[Dict]) -> str:
     return product_str
 
 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
+    # Highlight products if available
+    if products:
+        product = products[0]  # Prioritize the first matched product
+        product_highlight = f" How about our '{product['name']}'? It’s {product['description'].lower()}."
+        response += product_highlight
+
+    # Intent-specific tweaks
+    if intent == "recommend_product":
+        return response  # Product info already appended if available
     elif intent == "company_info":
-        return f"{response} At Hutter Products GmbH, we specialize in sustainable product design and production!"
+        return f"{response} Hutter Products GmbH specializes 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?"
+        return "I’m Hutter, your shopping guide for Hutter Products GmbH. How can I assist 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}!"
+                return f"{response} Also, {num1} plus {num2} is {num1 + num2}."
             elif op == "-":
-                return f"{num1} minus {num2} is {num1 - num2}!"
+                return f"{response} Also, {num1} minus {num2} is {num1 - num2}."
             elif op == "*":
-                return f"{num1} times {num2} is {num1 * num2}!"
+                return f"{response} Also, {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
+                return f"{response} Also, {num1} divided by {num2} is {num1 / num2}." if num2 != 0 else f"{response} Can’t divide by zero!"
+        return f"{response} I can handle simple math too—try something like '2 + 2'."
+    return response  # Default case includes products if matched
 
 # Endpoints
 @app.get("/")
@@ -148,7 +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} || {input_text}" if (history or product_context) else input_text
+        full_input = f"{context_msg} || {history_str} || {product_context} || {input_text}" if (history or product_context) else f"{context_msg} || {input_text}"
         logger.info(f"Full input to model: {full_input}")
 
         logger.info("Tokenizing input...")