Update app.py

app.py

@@ -27,19 +27,19 @@ text_category_example = [[" anadolu_efes e 18 lik star ! beko_basketbol_ligi nde
 
 
 @spaces.GPU
-def nli(input, model_choice="turna_nli_nli_tr"):
+def nli(input, model_choice="turna_nli_nli_tr", generation_params):
     
     if model_choice=="turna_nli_nli_tr":
-        nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0) 
+        nli_model = pipeline(model="boun-tabi-LMG/turna_nli_nli_tr", device=0, **generation_params) 
         return nli_model(input)[0]["generated_text"]
     else:
-        stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0)
+        stsb_model = pipeline(model="boun-tabi-LMG/turna_semantic_similarity_stsb_tr", device=0, **generation_params)
 
         return stsb_model(input)[0]["generated_text"]
 
 
 @spaces.GPU
-def sentiment_analysis(input, model_choice="turna_classification_17bintweet_sentiment"):
+def sentiment_analysis(input, model_choice="turna_classification_17bintweet_sentiment", generation_params):
     if model_choice=="turna_classification_17bintweet_sentiment":
         sentiment_model = pipeline(model="boun-tabi-LMG/turna_classification_17bintweet_sentiment", device=0) 
 
@@ -96,15 +96,16 @@ def categorize(input):
     return ttc(input)[0]["generated_text"]
 
 @spaces.GPU
-def turna(input):
+def turna(input, generation_params):
     turna = pipeline(model="boun-tabi-LMG/TURNA", device=0) 
 
-    return turna(input)[0]["generated_text"]
+    return turna(input, **generation_params)[0]["generated_text"]
 
 
 with gr.Blocks(theme="abidlabs/Lime") as demo:
     gr.Markdown("# TURNA 🐦")
     gr.Markdown(DESCRIPTION)
+
     
     with gr.Tab("Sentiment Analysis"):
         gr.Markdown("TURNA fine-tuned on sentiment analysis. Enter text to analyse sentiment and pick the model (tweets or product reviews).")
@@ -123,14 +124,31 @@ with gr.Blocks(theme="abidlabs/Lime") as demo:
         gr.Markdown("Pre-trained TURNA. Enter text to start generating.")
         with gr.Column():
             with gr.Row():
-                with gr.Column():
-                    text_gen_input = gr.Textbox(label="Text Generation Input")
+                max_new_tokens = gr.Slider(label = "Maximum length",
+                		minimum = 0,
+                		maximum = 512,
+                		value = 128)
+                length_penalty = gr.Slider(label = "Length penalty",
+                    		value=1.0)
+                top_k = gr.Slider("Top-k", value=10)
+                top_p = gr.Slider("Top-p", value=0.95)
+                temp = gr.Slider(label = "Temperature", value=1.0, minimum=0.1, maximum=100.0)
+                repetition_penalty = gr.Slider("Repetition Penalty", minimum=0.0, value=3.0, step=0.1)
+                num_beams = gr.Slider(label = "Number of beams", minimum=1,
+                    		maximum=10, value=3)
+                do_sample = gr.Radio(choices = [True, False], value = True, label = "Sampling")
+                generation_params = {"max_new_tokens":max_new_tokens, length_penalty:"length_penalty",
+                                    "top_k": top_k, "top_p": top_p, "temp": temp, "num_beams": num_beams,
+                                    "do_sample": do_sample}
+            with gr.Row():
                 
-                    text_gen_submit = gr.Button()
+                text_gen_input = gr.Textbox(label="Text Generation Input")
+            
+                text_gen_submit = gr.Button()
                 text_gen_output = gr.Textbox(label="Text Generation Output")
-                text_gen_submit.click(turna, inputs=[text_gen_input], outputs=text_gen_output)
+                text_gen_submit.click(turna, inputs=[text_gen_input, generation_params], outputs=text_gen_output)
             text_gen_example = [["Bir varmış, bir yokmuş, evvel zaman içinde, kalbur saman içinde, uzak diyarların birinde bir turna"]]
-            text_gen_examples = gr.Examples(examples = text_gen_example, inputs = [text_gen_input], outputs=text_gen_output, fn=turna)
+            text_gen_examples = gr.Examples(examples = text_gen_example, inputs = [text_gen_input, generation_params], outputs=text_gen_output, fn=turna)
         
     with gr.Tab("Text Categorization"):
         gr.Markdown("TURNA fine-tuned on text categorization. Enter text to categorize text or try the example.")