fix: class

app.py

@@ -179,46 +179,51 @@ class ModelHandler:
     def __init__(self):
         self.model = None
         self.tokenizer = None
-        
+        self._initialize_model()
+    
+    @staticmethod
     @st.cache_resource
-    def load_model(self):
+    def _load_model():
         """Load FLAN-T5 Small model with optimized settings"""
-        if self.model is None:
-            try:
-                self.tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-                self.model = T5ForConditionalGeneration.from_pretrained(
-                    MODEL_PATH,
-                    device_map={"": "cpu"},
-                    torch_dtype=torch.float32,
-                    low_cpu_mem_usage=True
-                )
-                return True
-            except Exception as e:
-                logging.error(f"Error loading model: {str(e)}")
-                return False
-        return True
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+            model = T5ForConditionalGeneration.from_pretrained(
+                MODEL_PATH,
+                device_map={"": "cpu"},
+                torch_dtype=torch.float32,
+                low_cpu_mem_usage=True
+            )
+            return model, tokenizer
+        except Exception as e:
+            logging.error(f"Error loading model: {str(e)}")
+            return None, None
+    
+    def _initialize_model(self):
+        """Initialize model and tokenizer"""
+        self.model, self.tokenizer = self._load_model()
 
     def generate_answer(self, question: str, context: str, max_length: int = 512) -> str:
         """Generate answer with FLAN-T5 optimized parameters"""
-        if not self.load_model():
+        if self.model is None or self.tokenizer is None:
             return "Error: Model loading failed. Please try again later."
         
         try:
             # FLAN-T5 responds better to direct instruction prompts
             input_text = f"""Answer the following question about autism using the provided research context.
-                Research Context:
-                {context}
 
-                Question: {question}
+Research Context:
+{context}
+
+Question: {question}
 
-                Instructions:
-                - Be specific and evidence-based
-                - Use clear, accessible language
-                - Focus on practical implications
-                - Cite research when relevant
-                - Be respectful of neurodiversity
+Instructions:
+- Be specific and evidence-based
+- Use clear, accessible language
+- Focus on practical implications
+- Cite research when relevant
+- Be respectful of neurodiversity
 
-                Answer:"""
+Answer:"""
             
             inputs = self.tokenizer(
                 input_text,
@@ -232,10 +237,10 @@ class ModelHandler:
                 outputs = self.model.generate(
                     **inputs,
                     max_length=max_length,
-                    min_length=100,  # Reduzido para FLAN-T5 Small
-                    num_beams=3,     # Ajustado para melhor performance
-                    length_penalty=1.0,  # Mais neutro para respostas concisas
-                    temperature=0.6,     # Mais determinístico
+                    min_length=100,
+                    num_beams=3,
+                    length_penalty=1.0,
+                    temperature=0.6,
                     repetition_penalty=1.2,
                     early_stopping=True,
                     no_repeat_ngram_size=2,
@@ -247,7 +252,7 @@ class ModelHandler:
             response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
             response = TextProcessor.clean_text(response)
             
-            if len(response.strip()) < 50:  # Ajustado para respostas mais curtas do FLAN-T5
+            if len(response.strip()) < 50:
                 return self._get_fallback_response()
             
             return self._format_response(response)
@@ -259,10 +264,10 @@ class ModelHandler:
     @staticmethod
     def _get_fallback_response() -> str:
         """Provide a structured fallback response"""
-        return """Based on the available research, I cannot provide a specific answer to your question. However, I can suggest:
+        return """Based on the available research, I cannot provide a specific answer to your question. Please try:
 
-1. Try rephrasing your question to focus on specific aspects of autism
-2. Consider asking about:
+1. Rephrasing your question to be more specific
+2. Asking about:
    - Specific behaviors or characteristics
    - Intervention strategies
    - Research findings
@@ -273,7 +278,6 @@ This will help me provide more accurate, research-based information."""
     @staticmethod
     def _format_response(response: str) -> str:
         """Format the response for better readability"""
-        # Add section headers
         sections = response.split('\n\n')
         formatted_sections = []