new version

app.py

@@ -1,12 +1,11 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v1.ipynb.
+# v1.1
 
 # %% auto 0
 __all__ = ['s_model', 'qa_model', 'question_1', 'question_2', 'question_3', 'question_4', 'question_5', 'question_6',
-           'question_7', 'question_8', 'question_9', 'question_10', 'contexts', 'answer_example', 'a_text', 'n_slider',
-           'intf', 'QA_similarity']
+           'question_7', 'question_8', 'question_9', 'question_10', 'contexts', 'data', 'context_df', 'a_text',
+           'n_slider', 'intf', 'QA_similarity']
 
-
-# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v1.ipynb 2
+# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 2
 import pandas as pd
 import gradio as gr
 
@@ -16,7 +15,7 @@ s_model = SentenceTransformer('clips/mfaq')
 from transformers import pipeline
 qa_model = pipeline("question-answering")
 
-# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v1.ipynb 3
+# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 3
 # Define the question(s)
 question_1 = "What are the main features of the new XPhone 20?"
 question_2 = "What are some benefits of regular exercise?"
@@ -73,12 +72,17 @@ contexts = [
     "The Declaration of Independence was adopted by the Continental Congress on July 4, 1776.",
 ]
 
-# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v1.ipynb 5
+# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 4
 # Function to find similar answers in a list of contexts
 def QA_similarity(question, contexts, n_answers=1):
 
+    if not isinstance(contexts, pd.DataFrame):
+        contexts = pd.read_csv(contexts)
+        contexts.rename(index={0: 'Context'})
+        contexts = contexts['Context'].to_list()
 
-    contexts = context_df['Context'].to_list()
+    else:
+      contexts = contexts['Context'].to_list()
 
     # to use 'clips/mfaq' questions need to be prepended with <Q>, and answers with <A>.
     question = "<Q>"+question
@@ -112,35 +116,25 @@ def QA_similarity(question, contexts, n_answers=1):
 
     return df
 
-# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v1.ipynb 7
-
+# %% ../drive/MyDrive/Codici/Python/Gradio_App/SemanticSearch_QA-v2.ipynb 6
 data = {
 'Context': contexts,
 }
 
 context_df = pd.DataFrame(data)
 
-a_text = gr.components.Dataframe(context_df,col_count=(1,"fixed"))
-
+a_text = gr.components.Dataframe(col_count=(1,"fixed"),interactive = True)
 n_slider = gr.components.Slider(minimum=1, maximum = 10, label = "Select n answers (max= 10)",step = 1)
 
 intf = gr.Interface(fn=QA_similarity,
                     inputs= ["text", a_text, n_slider],
                     outputs= gr.components.Dataframe(),
-                    examples=[
-                        [question_1],
-                        [question_2],
-                        [question_3],
-                        [question_4],
-                        [question_5],
-                        [question_6],
-                        [question_7],
-                        [question_8],
-                        [question_9],
-                        [question_10],],
+                    examples = [[question_1,context_df,3],
+                                [question_2,context_df,5],
+                                [question_4,context_df,10]],
                     debug=True,
 )
 
-intf.launch(inline=False,
+intf.launch(inline=True,
             # share=True
             )