Adding model

app.py

@@ -2,8 +2,51 @@ import gradio as gr
 
 # model = 
 
-def greet(sentiment):
-    return "Hello " + sentiment + "!!"
+# def greet(sentiment):
+#     return "Hello " + sentiment + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+import gradio as gr
+from NeuralTextGenerator import BertTextGenerator
+# from transformers import pipeline
+
+# generator = pipeline("sentiment-analysis")
+
+
+model_name = "JuanJoseMV/BERT_text_gen" #"dbmdz/bert-base-italian-uncased"
+en_model = BertTextGenerator(model_name)
+tokenizer = en_model.tokenizer
+model = en_model.model
+device = model.device
+
+def classify(sentiment):
+    parameters = {'n_sentences': 10,  
+              'batch_size': 2,
+              'avg_len':30,
+              'max_len':50,
+              # 'std_len' : 3,
+              'generation_method':'parallel',
+              'sample': True,
+              'burnin': 450,
+              'max_iter': 500,
+              'top_k': 100,
+              'seed_text': f"[{sentiment}-0] [{sentiment}-1] [{sentiment}-2]",
+            #   'verbose': True
+              }
+    sents = en_model.generate(**parameters)
+    gen_text = '\n'.join(sents)
+
+    return gen_text
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown()
+    inputs = gr.Dropdown(value=["POSITIVE", "NEGATIVE"], label="Sentiment to generate")
+    output = gr.Textbox(label="Generated tweet")
+    b1 = gr.Button("Generate")
+    b1.click(classify, inputs=inputs, outputs=output)
+
+demo.launch()