Update models.py

models.py

@@ -39,12 +39,6 @@ class BaseTCOModel(ABC):
     def set_name(self, name):
         self.name = name
     
-    def set_formula(self, formula):
-        self.formula = formula
-    
-    def get_formula(self):
-        return self.formula
-    
     def set_latency(self, latency):
         self.latency = latency
     
@@ -55,15 +49,6 @@ class OpenAIModel(BaseTCOModel):
 
     def __init__(self):
         self.set_name("(SaaS) OpenAI")
-        self.set_formula(r"""For the (SaaS) OpenAI service: <br>
-                         $CR = \frac{CIT\_1K \times IT + COT\_1K \times OT}{1000}$  <br>
-                         with: <br>
-                         CR = Cost per Request <br>
-                         CIT_1K = Cost per 1000 Input Tokens <br>
-                         COT_1K = Cost per 1000 Output Tokens <br>
-                         IT = Input Tokens <br>
-                         OT = Output Tokens
-                         """)
         self.latency = "15s" #Default value for GPT4
         super().__init__()
 
@@ -126,15 +111,6 @@ class OpenSourceLlama2Model(BaseTCOModel):
     
     def __init__(self):
         self.set_name("(Open source) Llama 2 70B")
-        self.set_formula(r"""For the (Open source) Llama 2 70B service: <br>
-                         $CR = \frac{CIT\_1K \times IT + COT\_1K \times OT}{1000}$  <br>
-                         with: <br>
-                         CR = Cost per Request <br>
-                         CIT_1K = Cost per 1000 Input Tokens <br>
-                         COT_1K = Cost per 1000 Output Tokens <br>
-                         IT = Input Tokens <br>
-                         OT = Output Tokens
-                         """)
         self.set_latency("27s")
         super().__init__()
     
@@ -173,18 +149,9 @@ class OpenSourceLlama2Model(BaseTCOModel):
         return cost_per_input_token,  cost_per_output_token, labor
 
 class CohereModel(BaseTCOModel):
-    
     def __init__(self):
         self.set_name("(SaaS) Cohere")
-        self.set_formula(r"""For the (SaaS) Cohere service: <br>
-                         $CR = \frac{CT\_1M \times (IT + OT)}{1000000}$  <br>
-                         with: <br>
-                         CR = Cost per Request <br>
-                         CT_1M = Cost per one million Tokens <br>
-                         IT = Input Tokens <br>
-                         OT = Output Tokens 
-                         """)
-        self.set_latency("")
+        self.set_latency("Not available")
         super().__init__()
     
     def render(self):
@@ -224,7 +191,6 @@ class CohereModel(BaseTCOModel):
         return cost_per_input_token, cost_per_output_token, labor
 
 class ModelPage:
-    
     def __init__(self, Models: BaseTCOModel):
         self.models: list[BaseTCOModel] = []
         for Model in Models:
@@ -272,9 +238,8 @@ class ModelPage:
                 model_args = args[begin:begin+model_n_args]
                 cost_per_input_token, cost_per_output_token, labor_cost = model.compute_cost_per_token(*model_args)
                 model_tco = cost_per_input_token * current_input_tokens + cost_per_output_token * current_output_tokens 
-                formula = model.get_formula()
                 latency = model.get_latency()
                 
-                return model_tco, formula, latency, labor_cost
+                return model_tco, latency, labor_cost
             
             begin = begin+model_n_args