both first demos now work

new_app.py

@@ -291,21 +291,23 @@ Given a protein sequence and a drug (in SMILES), estimate the binding affinity.
 
     def run_model(self, sample_dict, model: Mammal):
         # Generate Prediction
-        batch_dict = model.generate(
-            [sample_dict],
-            output_scores=True,
-            return_dict_in_generate=True,
-            max_new_tokens=5,
-        )
+        batch_dict = model.forward_encoder_only([sample_dict])
         return batch_dict
         
     def decode_output(self,batch_dict, model_holder):
 
         # Get output
-        generated_output = model_holder.tokenizer_op._tokenizer.decode(batch_dict[CLS_PRED][0])
-        score = batch_dict["model.out.scores"][0][1][self.positive_token_id(model_holder)].item()
-
-        return generated_output, score
+        batch_dict = DtiBindingdbKdTask.process_model_output(
+            batch_dict,
+            scalars_preds_processed_key="model.out.dti_bindingdb_kd",
+            norm_y_mean=5.79384684128215,
+            norm_y_std=1.33808027428196,
+            )
+        ans = (
+        "model.out.dti_bindingdb_kd",
+        float(batch_dict["model.out.dti_bindingdb_kd"][0]),
+        ) 
+        return ans
 
 
     def create_and_run_prompt(self,model_name,target_seq, drug_seq):
@@ -353,15 +355,11 @@ Given a protein sequence and a drug (in SMILES), estimate the binding affinity.
                 prompt_box = gr.Textbox(label="Mammal prompt", lines=5)
 
             with gr.Row():
-                decoded = gr.Textbox(label="Mammal output")
+                decoded = gr.Textbox(label="Mammal output key")
                 run_mammal.click(
                     fn=self.create_and_run_prompt,
                     inputs=[model_name_widget, target_textbox, drug_textbox],
-                    outputs=[prompt_box, decoded, gr.Number(label="PPI score")],
-                )
-            with gr.Row():
-                gr.Markdown(
-                    "```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interacting and ```<0>``` for non-interacting"
+                    outputs=[prompt_box, decoded, gr.Number(label="binding affinity")],
                 )
             demo.visible = False
             return demo