Update app.py

app.py

@@ -9,17 +9,17 @@ tokenizer = RobertaTokenizer.from_pretrained("imamnurby/rob2rand_chen_w_prefix_c
 
 from transformers import RobertaTokenizer, EncoderDecoderModel
 import pandas as pd
-def generate_taps(gen_mode, desc):
-    input_desc = desc.lower()
-    if gen_mode=="Channel":
-        input_desc = "GENERATE TRIGGER AND ACTION CHANNEL ONLY <pf> " + input_desc 
-    elif gen_mode=="Function":
-        input_desc = "GENERATE BOTH CHANNEL AND FUNCTION FOR TRIGGER AND ACTION <pf> " + input_desc 
+def generate_preds(desc):
+    # input_desc = desc.lower()
+    # if gen_mode=="Channel":
+    #     input_desc = "GENERATE TRIGGER AND ACTION CHANNEL ONLY <pf> " + input_desc 
+    # elif gen_mode=="Function":
+    #     input_desc = "GENERATE BOTH CHANNEL AND FUNCTION FOR TRIGGER AND ACTION <pf> " + input_desc 
     
     model = EncoderDecoderModel.from_pretrained("imamnurby/rob2rand_chen_w_prefix_c_fc")
     tokenizer = RobertaTokenizer.from_pretrained("imamnurby/rob2rand_chen_w_prefix_c_fc")
     
-    input_ids = tokenizer.encode(input_desc, return_tensors='pt')
+    input_ids = tokenizer.encode(desc, return_tensors='pt')
     
     # activate beam search and early_stopping
     preds = model.generate(
@@ -34,30 +34,28 @@ def generate_taps(gen_mode, desc):
     for item in preds:
         output_list.append(tokenizer.decode(item, skip_special_tokens=True))
     
-    if gen_mode=="Channel":
-        trigger = [x.split("<sep>")[0].strip() for x in output_list]
-        # trigger_desc = ["dummy" for x in output_list]
-        action = [x.split("<sep>")[1].strip() for x in output_list]
-        # action_desc = ["dummy" for x in output_list]
-        df = {"Trigger": trigger,
-              # "Trigger Description": trigger_desc,
-              "Action": action,
-              # "Action Description": action_desc
-             }
-    elif gen_mode=="Function":
-        trigger = [x.split("<sep>")[1].strip() for x in output_list]
-        trigger_desc = ["dummy" for x in output_list]
-        action = [x.split("<sep>")[3].strip() for x in output_list]
-        action_desc = ["dummy" for x in output_list]
-        df = {"Trigger": trigger,
-              # "Trigger Description": trigger_desc,
-              "Action": action,
-              # "Action Description": action_desc
-             }
+    # if gen_mode=="Channel":
+    trigger = [x.split("<sep>")[0].strip() for x in output_list]
+    # trigger_desc = ["dummy" for x in output_list]
+    action = [x.split("<sep>")[1].strip() for x in output_list]
+    # action_desc = ["dummy" for x in output_list]
+    df = {"Trigger": trigger,
+          # "Trigger Description": trigger_desc,
+          "Action": action,
+          # "Action Description": action_desc
+         }
+    # elif gen_mode=="Function":
+    #     trigger = [x.split("<sep>")[1].strip() for x in output_list]
+    #     trigger_desc = ["dummy" for x in output_list]
+    #     action = [x.split("<sep>")[3].strip() for x in output_list]
+    #     action_desc = ["dummy" for x in output_list]
+    #     df = {"Trigger": trigger,
+    #           # "Trigger Description": trigger_desc,
+    #           "Action": action,
+    #           # "Action Description": action_desc
+    #          }
     return pd.DataFrame(df)
 
-
-demo = gr.Blocks()
 with demo:
     gr.Markdown("<h1><center>RecipeGen: Automated TAPs Generation Tool</center></h1>")
     gr.Markdown("<center>This demo allows you to generate TAPs (Trigger Action Programs) using functionality description described in English. You can learn the working detail of our tool from our paper<center>")
@@ -70,27 +68,27 @@ with demo:
     5. Click **Generate**; the generated TAPs along with the description of each component will show in the **Results**
     """)
     gr.Markdown("NOTE: **#Returned Sequences** should be LESS THAN OR EQUAL **Beam Width**")
-    with gr.Tabs():
-        with gr.TabItem("Channel/Function"):    
-            with gr.Column():
-                gen_mode = gr.Radio(label="Granularity", choices=["Channel", "Function"])
-                desc = gr.Textbox(label="Functionality Description", placeholder="Describe the functionality here")
-                num_beams = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="Beam Width")
-                num_returned_seqs = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="#Returned Sequences")
-                with gr.Row():
-                    generate = gr.Button("Generate")
-                gr.Markdown("<h1><center>Results</center></h1>")
-                results = gr.Dataframe(headers=["Trigger", "Trigger Description", "Action", "Action Description"])
+    # with gr.Tabs():
+    #     with gr.TabItem("Channel/Function"):    
+    #         with gr.Column():
+    # gen_mode = gr.Radio(label="Granularity", choices=["Channel", "Function"])
+    desc = gr.Textbox(label="Functionality Description", placeholder="Describe the functionality here")
+    # num_beams = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="Beam Width")
+    # num_returned_seqs = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="#Returned Sequences")
+    # with gr.Row():
+    generate = gr.Button("Generate")
+    # gr.Markdown("<h1><center>Results</center></h1>")
+    results = gr.Dataframe(headers=["Trigger", "Trigger Description", "Action", "Action Description"])
         
-        with gr.TabItem("Field"):    
-            with gr.Column():
-                desc = gr.Textbox(label="Functionality Description", placeholder="Describe the functionality here")
-                num_beams = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="Beam Width")
-                num_returned_seqs = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="#Returned Sequences")
-                with gr.Row():
-                    generate_field = gr.Button("Generate")
-                gr.Markdown("<h1><center>Results</center></h1>")
-                results_field = gr.Dataframe(headers=["Trigger", "Trigger Description", "Trigger Fields", "Action", "Action Description", "Action Fields"])
+        # with gr.TabItem("Field"):    
+        #     with gr.Column():
+                # desc = gr.Textbox(label="Functionality Description", placeholder="Describe the functionality here")
+                # num_beams = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="Beam Width")
+                # num_returned_seqs = gr.Slider(minimum=2, maximum=500, value=2, step=1, label="#Returned Sequences")
+                # with gr.Row():
+                #     generate_field = gr.Button("Generate")
+                # gr.Markdown("<h1><center>Results</center></h1>")
+                # results_field = gr.Dataframe(headers=["Trigger", "Trigger Description", "Trigger Fields", "Action", "Action Description", "Action Fields"])
     
-    generate.click(generate_taps, inputs=[gen_mode, desc], outputs=[results])
+    generate.click(generate_preds, inputs=[desc], outputs=[results])
 demo.launch()