Upload app.py

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+from data_library import embedded_form
+
+import faiss
+embedded_form=embedded_form["train"]
+embedded_form.load_faiss_index('embeddings', 'my_index.faiss')
+
+
+
+# gradio function
+
+description="""<center>
+<H1 style="background-color:powderblue;">ASK ME SCIENCE RELATED QUESTIONS(BIOLOGY,PHYSICS AND CHEMISTRY)</H1></center>"""
+
+
+def input_text1(text):
+
+  
+  question_embedding =sample_embedding([text])
+  question_embedding=question_embedding["embedding"]
+  scores, samples = embedded_form.get_nearest_examples(
+      "embedding", question_embedding, k=5
+    )
+  dataframe=pd.DataFrame(samples)
+  dataframe["scores"]=scores
+  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)
+
+  return dataframe.loc[0,"support"]
+
+
+
+def input_text2(text):
+
+  
+  question_embedding =sample_embedding([text])
+  question_embedding=question_embedding["embedding"]
+  scores, samples = embedded_form.get_nearest_examples(
+      "embedding", question_embedding, k=5
+    )
+  dataframe=pd.DataFrame(samples)
+  dataframe["scores"]=scores
+  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)
+
+  return dataframe.loc[1,"support"]
+
+
+def input_text3(text):
+
+  
+  question_embedding =sample_embedding([text])
+  question_embedding=question_embedding["embedding"]
+  scores, samples = embedded_form.get_nearest_examples(
+      "embedding", question_embedding, k=5
+    )
+  dataframe=pd.DataFrame(samples)
+  dataframe["scores"]=scores
+  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)
+
+  return dataframe.loc[2,"support"]
+
+
+def input_text4(text):
+
+  
+  question_embedding =sample_embedding([text])
+  question_embedding=question_embedding["embedding"]
+  scores, samples = embedded_form.get_nearest_examples(
+      "embedding", question_embedding, k=5
+    )
+  dataframe=pd.DataFrame(samples)
+  dataframe["scores"]=scores
+  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)
+
+  return dataframe.loc[3,"support"]
+
+def input_text5(text):
+
+  
+  question_embedding =sample_embedding([text])
+  question_embedding=question_embedding["embedding"]
+  scores, samples = embedded_form.get_nearest_examples(
+      "embedding", question_embedding, k=5
+    )
+  dataframe=pd.DataFrame(samples)
+  dataframe["scores"]=scores
+  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)
+
+  return dataframe.loc[4,"support"]
+
+
+answer1=gr.Interface(input_text1,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 1"))
+
+answer2=gr.Interface(input_text2,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 2"))
+
+answer3=gr.Interface(input_text3,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 3"))
+
+
+answer4=gr.Interface(input_text4,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 4"))
+
+answer5=gr.Interface(input_text5,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 5"))
+
+demo=gr.Parallel(answer1,answer2,answer3,answer4,answer5,description=description)
+
+demo.launch()
+  
+