Adding components

app.py

@@ -14,7 +14,7 @@ from NeuralTextGenerator import BertTextGenerator
 
 # generator = pipeline("sentiment-analysis")
 
-print('dfg')
+# print('dfg')
 model_name = "JuanJoseMV/BERT_text_gen" #"dbmdz/bert-base-italian-uncased"
 en_model = BertTextGenerator(model_name)
 tokenizer = en_model.tokenizer
@@ -24,8 +24,41 @@ device = model.device
 en_model.tokenizer.add_special_tokens({'additional_special_tokens': ['[POSITIVE-0]', '[POSITIVE-1]', '[POSITIVE-2]','[NEGATIVE-0]', '[NEGATIVE-1]', '[NEGATIVE-2]']})
 en_model.model.resize_token_embeddings(len(en_model.tokenizer))
 
-def classify(sentiment):
-    parameters = {'n_sentences': 1,  
+# def classify(sentiment):
+#     parameters = {'n_sentences': 1,  
+#               'batch_size': 2,
+#               'avg_len':30,
+#               'max_len':50,
+#               # 'std_len' : 3,
+#               'generation_method':'parallel',
+#               'sample': True,
+#               'burnin': 450,
+#               'max_iter': 100,
+#               'top_k': 100,
+#               'seed_text': f"[{sentiment}-0] [{sentiment}-1] [{sentiment}-2] Ronaldo",
+#             #   'verbose': True
+#               }
+#     sents = en_model.generate(**parameters)
+#     gen_text = ''
+
+#     for i, s in enumerate(sents):
+#         gen_text += f'- GENERATED TWEET #{i}: {s}\n'
+
+#     return gen_text
+
+# demo = gr.Blocks()
+
+# with demo:
+#     gr.Markdown()
+#     inputs = gr.Radio(["POSITIVE", "NEGATIVE"], label="Sentiment to generate") # gr.Dropdown(["POSITIVE", "NEGATIVE"], label="Sentiment to generate")
+#     output = gr.Textbox(label="Generated tweet")
+#     b1 = gr.Button("Generate")
+#     b1.click(classify, inputs=inputs, outputs=output)
+
+
+
+def sentence_builder(n_sentences, max_iter, sentiment, seed_text):
+    parameters = {'n_sentences': n_sentences,  
               'batch_size': 2,
               'avg_len':30,
               'max_len':50,
@@ -33,9 +66,9 @@ def classify(sentiment):
               'generation_method':'parallel',
               'sample': True,
               'burnin': 450,
-              'max_iter': 100,
+              'max_iter': max_iter,
               'top_k': 100,
-              'seed_text': f"[{sentiment}-0] [{sentiment}-1] [{sentiment}-2] Ronaldo",
+              'seed_text': f"[{sentiment}-0] [{sentiment}-1] [{sentiment}-2] {seed_text}",
             #   'verbose': True
               }
     sents = en_model.generate(**parameters)
@@ -45,14 +78,18 @@ def classify(sentiment):
         gen_text += f'- GENERATED TWEET #{i}: {s}\n'
 
     return gen_text
+    # return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
 
-demo = gr.Blocks()
 
-with demo:
-    gr.Markdown()
-    inputs = gr.Radio(["POSITIVE", "NEGATIVE"], label="Sentiment to generate") # gr.Dropdown(["POSITIVE", "NEGATIVE"], label="Sentiment to generate")
-    output = gr.Textbox(label="Generated tweet")
-    b1 = gr.Button("Generate")
-    b1.click(classify, inputs=inputs, outputs=output)
+demo = gr.Interface(
+    sentence_builder,
+    [
+        gr.Slider(1, 15, value=2, label="Num. Tweets", info="Number of tweets to be generated."),
+        gr.Slider(50, 500, value=100, label="Max. iter", info="Maximum number of iterations for the generation."),
+        gr.Radio(["POSITIVE", "NEGATIVE"], label="Sentiment to generate"),
+        gr.Textbox('', label="Seed text", info="Seed text for the generation.")
+    ],
+    "text",
+)
 
 demo.launch()