Update app.py

app.py

@@ -39,16 +39,27 @@ model = LLaMAForCausalLM.from_pretrained(
 ########## LOADING PRE-COMPUTED EMBEDDINGS ##########
 class_embeddings = pd.read_csv('Embeddings/MainClassEmbeddings.csv')
 
+"""
 abstract = """
 #Described herein are strength characteristics and biodegradation of articles produced using one or more “green” sustainable polymers and one or more carbohydrate-based polymers. A compatibilizer can optionally be included in the article. In some cases, the article can include a film, a bag, a bottle, a cap or lid therefore, a sheet, a box or other container, a plate, a cup, utensils, or the like.
 """
-
 abstract= classification.clean_data(abstract, type='String')
 abstract_embedding = classification.sentence_embedder(abstract, 'Model_bert')
 Number = 10
 broad_scope_predictions = classification.broad_scope_class_predictor(class_embeddings, abstract_embedding, Number, Sensitivity='High')
 
 print(broad_scope_class_predictor)
+"""
+
+def classifier(userin):
+    clean_in = classification.clean_data(userin, type='String')
+    in_emb = classification.sentence_embedder(clean_in, 'Model_bert')
+
+    Number = 10
+    broad_scope_predictions = classification.broad_scope_class_predictor(class_embeddings, abstract_embedding, Number, Sensitivity='High')
+
+    return broad_scope_predictions
+    
 
 def generateresponse(history):
     """
@@ -98,6 +109,7 @@ def generateresponse(history):
     print(history)
     return history
 
+
 theme = gr.themes.Base(
     primary_hue="indigo",
 ).set(
@@ -183,9 +195,11 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
         
         """)
         with gr.Row(scale=1, min_width=600):
-            text1 = gr.Textbox(label="Input",
+            userin = gr.Textbox(label="Input",
                               placeholder='Type in your Claim/Description/Abstract Here')
-            text2 = gr.Textbox(label="Output")
+            output = gr.Textbox(label="Output")
+            classify_btn = gr.Button("Classify")
+            classify_btn.click(fn=classifier, inputs=userin, outputs=output)
 
 
     gr.Markdown("""