app.py beta 2 (push 4)

app.py

@@ -57,7 +57,7 @@ class CombinedResponse(BaseModel):
 
 # Helper Functions
 def extract_keywords(text: str) -> List[str]:
-    doc = nlp(text)
+    doc Rustdocdoc = nlp(text)
     keywords = [token.text for token in doc if token.pos_ in ["NOUN", "PROPN"]]
     return list(set(keywords))
 
@@ -72,7 +72,7 @@ def detect_intent(text: str) -> str:
     return "unknown"
 
 def search_products_by_keywords(keywords: List[str]) -> List[Dict[str, Any]]:
-    if not keywords:  # Handle empty keywords
+    if not keywords:
         logger.info("No keywords provided, returning empty product list.")
         return []
     query = {"$or": [{"name": {"$regex": keyword, "$options": "i"}} for keyword in keywords]}
@@ -118,8 +118,15 @@ async def process_prompt(request: PromptRequest):
         full_input = f"{history_str} || {product_context} {context_msg} || {input_text}" if history else f"{product_context} {context_msg} || {input_text}"
         logger.info(f"Full input to model: {full_input}")
 
+        logger.info("Tokenizing input...")
         inputs = tokenizer(full_input, return_tensors="pt", truncation=True, max_length=512)
-        outputs = model.generate(**inputs, max_length=150, num_beams=5, no_repeat_ngram_size=2)
+        logger.info("Input tokenized successfully.")
+
+        logger.info("Generating model response...")
+        outputs = model.generate(**inputs, max_length=50, num_beams=1, no_repeat_ngram_size=2)
+        logger.info("Model generation complete.")
+
+        logger.info("Decoding model output...")
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
         logger.info(f"Model response: {response}")
 
@@ -130,6 +137,7 @@ async def process_prompt(request: PromptRequest):
             "score": 1.0
         }
 
+        logger.info("Returning response...")
         return {
             "ner": {"extracted_keywords": keywords},
             "qa": qa_response,