Update app.py

app.py

@@ -66,14 +66,14 @@ def cstr(s, color='black'):
 def cstr_bold(s, color='black'):
     return "<text style=color:{}><b>{}</b></text>".format(color, s)
 def cstr_break(s, color='black'):
-    return "<text style=color:{}>{}<br></text>".format(color, s)
+    return "<text style=color:{}><br>{}</text>".format(color, s)
 
 def print_colored(text, start_idx, end_idx, confidence):
     conf_str = 'Confidence: ' +  confidence
     a = cstr(' '.join([text[:start_idx], \
-                        cstr_bold(text[start_idx:end_idx], color='red'), \
+                        cstr_bold(text[start_idx:end_idx], color='blue'), \
                         text[end_idx:], \
-                        cstr_break(conf_str, color='yellow')]), color='black')
+                        cstr_break(conf_str, color='grey')]), color='black')
     return a
        
 def final_qa_pipeline(file, query):
@@ -91,13 +91,18 @@ def final_qa_pipeline(file, query):
         fnl_rank = qa_ranker(query, [l["docs"] for l in lvl1], top_k_ranker)
         #return (fnl_rank[0]["answer"], str(np.round(100*fnl_rank[0]["score"],2))+"%" , fnl_rank[0]['doc'])
         #return (print_colored(fnl_rank[0]['doc'], fnl_rank[0]['start'], fnl_rank[0]['end']), str(np.round(100*fnl_rank[0]["score"],2))+"%"
-        return (print_colored(fnl_rank[0]['doc'], fnl_rank[0]['start'], fnl_rank[0]['end'], str(np.round(100*fnl_rank[0]["score"],2))+"%")  )
+        top1 = print_colored(fnl_rank[0]['doc'], fnl_rank[0]['start'], fnl_rank[0]['end'], str(np.round(100*fnl_rank[0]["score"],2))+"%")  
+        if len(lvl1)>1:
+            top2 = print_colored(fnl_rank[1]['doc'], fnl_rank[1]['start'], fnl_rank[1]['end'], str(np.round(100*fnl_rank[1]["score"],2))+"%")
+        else:
+            top2 = "None"
+        return (top1, top2)
         #for fnl_ in fnl_rank:
         #    print("\n")
         #    print_colored(fnl_['doc'], fnl_['start'], fnl_['end'])
         #    print(colored("Confidence score of ") + colored(str(fnl_['score'])[:4], attrs=['bold']))
     else:
-        return ("No match")
+        return ("No match","No match")
         
 examples = [
     [os.path.abspath("dbs-annual-report-2020.pdf"), "how much dividend was paid to shareholders ?"],
@@ -111,7 +116,7 @@ examples = [
 iface = gr.Interface(
    fn = final_qa_pipeline,
    inputs = [gr.inputs.File(label="input pdf file"), gr.inputs.Textbox(label="Question:")],
-   outputs = [gr.outputs.HTML(label="Predicted answer")],
+   outputs = [gr.outputs.HTML(label="Top 1 answer"), gr.outputs.HTML(label="Top 2 answer")],
    examples=examples,
    title = "Question Answering on company annual reports",
    description = "Navigate long annual reports by using Machine learning to answer your questions. \nSimply upload any annual report pdf you are interested in and ask model a question OR load an example from below."