Update models.py

models.py

@@ -65,19 +65,19 @@ class OpenAIModelGPT4(BaseTCOModel):
         self.context_length = gr.Dropdown(["128K"], value="128K", interactive=True,
                                           label="Context size",
                                           visible=False, info="Number of tokens the model considers when processing text")
-        self.input_tokens_cost_per_token = gr.Number(0.01, visible=False,
-                                           label="($) Price/1K input prompt tokens",
+        self.input_tokens_cost_per_token = gr.Number(0.0095, visible=False,
+                                           label="(€) Price/1K input prompt tokens",
                                            interactive=False
                                            )
-        self.output_tokens_cost_per_token = gr.Number(0.03, visible=False,
-                                           label="($) Price/1K output prompt tokens",
+        self.output_tokens_cost_per_token = gr.Number(0.028, visible=False,
+                                           label="(€) Price/1K output prompt tokens",
                                            interactive=False
                                            )
         self.info = gr.Markdown("The cost per input and output tokens values are from OpenAI's [pricing web page](https://openai.com/pricing)", interactive=False, visible=False)
         self.context_length.change(define_cost_per_token, inputs=self.context_length, outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token])
         
-        self.labor = gr.Number(1000, visible=False, 
-                                label="($) Labor cost per month", 
+        self.labor = gr.Number(2000, visible=False, 
+                                label="(€) Labor cost per month", 
                                 info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model§/maitenance",
                                 interactive=True
                                 )
@@ -112,17 +112,17 @@ class MistralO(BaseTCOModel):
                                           label="Context size",
                                           visible=False, info="Number of tokens the model considers when processing text")
         self.input_tokens_cost_per_token = gr.Number(0.0025, visible=False,
-                                           label="($) Price/1K input prompt tokens",
+                                           label="(€) Price/1K input prompt tokens",
                                            interactive=False
                                            )
         self.output_tokens_cost_per_token = gr.Number(0.0075, visible=False,
-                                           label="($) Price/1K output prompt tokens",
+                                           label="(€) Price/1K output prompt tokens",
                                            interactive=False
                                            )
         self.info = gr.Markdown("The cost per input and output tokens values are from Mistral API", interactive=False, visible=False)
         self.context_length.change(define_cost_per_token, inputs=self.context_length, outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token])
         
-        self.labor = gr.Number(1000, visible=False, 
+        self.labor = gr.Number(2000, visible=False, 
                                 label="($) Labor cost per month", 
                                 info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model/maitenance",
                                 interactive=True
@@ -201,7 +201,7 @@ class DIYLlama2Model70(BaseTCOModel):
                               visible=False,
                               label="Instance of VM with GPU",
                               )
-        self.vm_cost_per_hour = gr.Number(4.42, label="Instance cost ($) per hour",
+        self.vm_cost_per_hour = gr.Number(4.05, label="Instance cost (€) per hour",
                                       interactive=False, visible=False)
         self.info_vm = gr.Markdown("This price above is from [CoreWeave's pricing web page](https://www.coreweave.com/gpu-cloud-pricing)", interactive=False, visible=False)
         self.maxed_out = gr.Slider(minimum=1, maximum=100, value=65, step=1, label="Maxed out", info="Estimated average percentage of total GPU memory that is used. The instantaneous value can go from very high when many users are using the service to very low when no one does.", visible=False)
@@ -214,19 +214,19 @@ class DIYLlama2Model70(BaseTCOModel):
                                           $TS_{max}$ = Tokens per second when the GPU is maxed out at 100%, <br>
                                           $MO$ = Maxed Out, <br>
                                           """, interactive=False, visible=False)
-        self.input_tokens_cost_per_token = gr.Number(0.00052, visible=False,
-                                           label="($) Price/1K input prompt tokens",
+        self.input_tokens_cost_per_token = gr.Number(0.00046, visible=False,
+                                           label="(€) Price/1K input prompt tokens",
                                            interactive=False
                                            )
-        self.output_tokens_cost_per_token = gr.Number(0.06656, visible=False,
-                                           label="($) Price/1K output prompt tokens",
+        self.output_tokens_cost_per_token = gr.Number(0.061, visible=False,
+                                           label="(€) Price/1K output prompt tokens",
                                            interactive=False
                                            )
         self.maxed_out.change(on_maxed_out_change, inputs=[self.maxed_out, self.input_tokens_cost_per_token, self.output_tokens_cost_per_token], outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token])
         
         self.labor = gr.Number(5000, visible=False, 
-                                label="($) Labor cost per month",
-                                info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model",
+                                label="(€) Labor cost per month",
+                                info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model/maitenance",
                                 interactive=True
                                 )
 
@@ -258,7 +258,7 @@ class DIYLlama2Model(BaseTCOModel):
                               visible=False,
                               label="Instance of VM with GPU",
                               )
-        self.vm_cost_per_hour = gr.Number(2.58, label="Instance cost ($) per hour",
+        self.vm_cost_per_hour = gr.Number(2.37, label="Instance cost (€) per hour",
                                       interactive=False, visible=False)
         self.info_vm = gr.Markdown("This price above is from [CoreWeave's pricing web page](https://www.coreweave.com/gpu-cloud-pricing)", interactive=False, visible=False)
         self.maxed_out = gr.Slider(minimum=1, maximum=100, value=65, step=1, label="Maxed out", info="Estimated average percentage of total GPU memory that is used. The instantaneous value can go from very high when many users are using the service to very low when no one does.", visible=False)
@@ -271,17 +271,17 @@ class DIYLlama2Model(BaseTCOModel):
                                           $TS_{max}$ = Tokens per second when the GPU is maxed out at 100%, <br>
                                           $MO$ = Maxed Out, <br>
                                           """, interactive=False, visible=False)
-        self.input_tokens_cost_per_token = gr.Number(0.00032, visible=False,
+        self.input_tokens_cost_per_token = gr.Number(0.00029, visible=False,
                                            label="($) Price/1K input prompt tokens",
                                            interactive=False
                                            )
-        self.output_tokens_cost_per_token = gr.Number(0.02656, visible=False,
+        self.output_tokens_cost_per_token = gr.Number(0.0024, visible=False,
                                            label="($) Price/1K output prompt tokens",
                                            interactive=False
                                            )
         self.maxed_out.change(on_maxed_out_change, inputs=[self.maxed_out, self.input_tokens_cost_per_token, self.output_tokens_cost_per_token], outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token])
         
-        self.labor = gr.Number(3000, visible=False, 
+        self.labor = gr.Number(5000, visible=False, 
                                 label="(€) Labor cost per month",
                                 info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model/Maitenance",
                                 interactive=True