Update app.py

app.py

@@ -24,56 +24,32 @@ def predict_sentiment(text):
         return 'Positive'
 
 def predict(sample, validate=True):
-  classifier = pipeline("text-classification", model=model_2, tokenizer=tokenizer_2)
   pred = classifier(sample)[0]['label']
   return pred
 
-# Input from user
-in_prompt_1 = gradio.components.Textbox(lines=10, placeholder=None, label='Enter review text')
-
-# Output response
-out_response_1 = gradio.components.Textbox(type="text", label='Sentiment')
-
-# Gradio interface to generate UI link
-title_1 = "Sentiment Classification"
-description_1 = "Analyse sentiment of the given review"
-
-iface_1 = gradio.Interface(fn = predict_sentiment,
-                         inputs = [in_prompt_1],
-                         outputs = [out_response_1],
-                         title = title_1,
-                         description = description_1)
-
-title_2 = "Symptoms and Disease"
-description_2 = "Enter the Symptoms to know the disease"
-
-# Input from user
-in_prompt_2 = gradio.components.Textbox(lines=2, label='Enter the Symptoms')
-
-# Output response
-out_response_2 = gradio.components.Textbox(label='Disease')
-
-# Gradio interface to generate UI link
-iface_2 = gradio.Interface(fn=predict,
-                         inputs = [in_prompt_2],
-                         outputs = [out_response_2],
-                         title=title_2,
-                         description=description_2
-                         )
-
-def combined_interface(input1, input2):
-  # Call individual model interfaces and combine results
-  model1_output = iface_1(input1)
-  model2_output = iface_2(input2)
-
-  combined_output = {
-    "model1_output": model1_output,
-    "model2_output": model2_output
-  }
-
-  return combined_output
-
-parent_interface = gradio.Interface(fn=combined_interface, inputs=["text","text"], outputs=["model1_output", "model2_output"])
-
-parent_interface.launch(debug = True)
-#iface.launch(debug = True)#, server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface
+def make_block(dem):
+    with dem:
+        gr.Markdown("""
+# Ejemplo de `space` multiclassifier: Curso Platzi""")
+        with gr.Tabs():
+            with gr.TabItem("Transcribe audio en español"):
+                with gr.Row():
+                    in_prompt_1 = gradio.components.Textbox(lines=10, placeholder=None, label='Enter review text')
+                    out_response_1 = gradio.components.Textbox(type="text", label='Sentiment')
+                b1 = gr.Button("Voz a Texto")
+
+            with gr.TabItem("Análisis de sentimiento en español"):
+                with gr.Row():
+                    in_prompt_2 = gradio.components.Textbox(lines=2, label='Enter the Symptoms')
+                    out_response_2 = gradio.components.Textbox(label='Disease')
+                b2 = gr.Button("Texto a Sentimiento")
+            b1.click(predict_sentiment, inputs=in_prompt_1, outputs=out_response_1)
+            b2.click(predict, inputs=in_prompt_2, outputs=out_response_2)
+           
+if __name__ == '__main__':
+    model_1 = pipeline(model= repo_path)
+    classifier = pipeline("text-classification", model=model_2, tokenizer=tokenizer_2)
+
+    demo = gr.Blocks()
+    make_block(demo)
+    demo.launch()