Update app.py

app.py

@@ -6,12 +6,12 @@ from transformers import (
     AutoTokenizer
 )
 
-M0 = "anshoomehra/question-generation-auto-t5-v1-base-s"
-M1 = "anshoomehra/question-generation-auto-t5-v1-base-s-q"
-M2 = "anshoomehra/question-generation-auto-t5-v1-base-s-q-c" 
+M0 = "consciousAI/question-generation-auto-t5-v1-base-s"
+M1 = "consciousAI/question-generation-auto-t5-v1-base-s-q"
+M2 = "consciousAI/question-generation-auto-t5-v1-base-s-q-c" 
 
-M4 = "anshoomehra/question-generation-auto-hints-t5-v1-base-s-q"
-M5 = "anshoomehra/question-generation-auto-hints-t5-v1-base-s-q-c" 
+M4 = "consciousAI/question-generation-auto-hints-t5-v1-base-s-q"
+M5 = "consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c" 
 
 device = ['cuda' if torch.cuda.is_available() else 'cpu'][0]
 
@@ -50,7 +50,7 @@ def _formatQs(questions):
                 _finalQs = None
     return _finalQs
     
-def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenalty=2.0, earlyStopping=True, numReturnSequences=1, numBeams=2, noRepeatNGramSize=0, doSample=False, topK=0, topP=0, temperature=0):
+def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenalty=2.0, earlyStopping=True, numReturnSequences=1, numBeams=2, noRepeatNGramSize=0, doSample=False, topK=0, penaltyAlpha=0, topP=0, temperature=0, model="All"):
           
     predictionM0 = None
     predictionM1 = None
@@ -60,7 +60,8 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
     
     if mode == 'Auto':
         _inputText = "question_context: " + context
-
+        if model == "All":
+        
         _encoding = _tk0.encode(_inputText, return_tensors='pt', truncation=True, padding='max_length').to(device) # max_length=1024 
         _outputEncoded = _m0.generate(_encoding, 
                                    min_length=minLength, 
@@ -72,6 +73,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -88,6 +90,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -104,6 +107,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -120,6 +124,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -136,6 +141,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -154,6 +160,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -170,6 +177,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
                                    no_repeat_ngram_size=noRepeatNGramSize,
                                    do_sample=doSample,
                                    top_k=topK,
+                                   penalty_alpha=penaltyAlpha,
                                    top_p=topP,
                                    temperature=temperature
                                )
@@ -186,7 +194,7 @@ def _generate(mode, context, hint=None, minLength=50, maxLength=500, lengthPenal
 with gr.Blocks() as demo:
     gr.Markdown(value="# Question Generation Demo \n [question-generation-auto-t5-v1-base-s](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s) ✫ [question-generation-auto-t5-v1-base-s-q](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s-q) ✫ [question-generation-auto-t5-v1-base-s-q-c](https://huggingface.co/anshoomehra/question-generation-auto-t5-v1-base-s-q-c) ✫ [question-generation-auto-hints-t5-v1-base-s-q](https://huggingface.co/anshoomehra/question-generation-auto-hints-t5-v1-base-s-q) ✫ [question-generation-auto-hints-t5-v1-base-s-q-c](https://huggingface.co/anshoomehra/question-generation-auto-hints-t5-v1-base-s-q-c)\n\n Please be patient, 5 models may take up to 80 sec to run on CPU")
 
-    with gr.Accordion(variant='compact', label='Hyperparams', open=False):
+    with gr.Accordion(variant='compact', label='Search Methods: Deteriminstic / Stochastic / Contrastive', open=True):
         with gr.Row():
             mode = gr.Radio(["Auto", "Hints"], value="Auto", label="Mode")
         with gr.Row():   
@@ -201,8 +209,12 @@ with gr.Blocks() as demo:
         with gr.Row():
             doSample = gr.Checkbox(label="Do Random Sample")
             topK = gr.Slider(0, 50, 0, step=1, label="Top K")
+            penaltyAlpha = gr.Slider(0.0, 1, 0, label="Penalty Alpha")
             topP = gr.Slider(0, 1, 0, label="Top P/Nucleus Sampling")
             temperature = gr.Slider(0.01, 1, 1, label="Temperature") 
+        with gr.Row():
+            model = gr.Dropdown(["question-generation-auto-hints-t5-v1-base-s-q-c", "question-generation-auto-hints-t5-v1-base-s-q", "question-generation-auto-t5-v1-base-s-q-c", "question-generation-auto-t5-v1-base-s-q", "question-generation-auto-t5-v1-base-s", "All"], label="Model")    
+         
     
     with gr.Accordion(variant='compact', label='Input Values'):
         with gr.Row(variant='compact'):
@@ -225,7 +237,7 @@ with gr.Blocks() as demo:
     with gr.Row():       
         gen_btn = gr.Button("Generate Questions")
         gen_btn.click(fn=_generate,
-                      inputs=[mode, context, hint, minLength, maxLength, lengthPenalty, earlyStopping, numReturnSequences, numBeams, noRepeatNGramSize, doSample, topK, topP, temperature],
+                      inputs=[mode, context, hint, minLength, maxLength, lengthPenalty, earlyStopping, numReturnSequences, numBeams, noRepeatNGramSize, doSample, topK, penaltyAlpha, topP, temperature, model],
                       outputs=[_predictionM5, _predictionM4, _predictionM2, _predictionM1, _predictionM0]
                       )