Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr 
+import pandas as pd  
+from transformers import AutoModelForSeq2SeqLM, AutoModelForTableQuestionAnswering, AutoTokenizer, pipeline 
+
+model_tapas = "google/tapas-large-finetuned-wtq"
+
+tokenizer_tapas = AutoTokenizer.from_pretrained(model_tapas) 
+pipe_tapas = pipeline("table-question-answering", model=model_tapas, tokenizer=tokenizer_tapas) 
+
+def process(query, file, correct_answer): 
+    table = pd.read_csv(file.name).astype(str).fillna('') 
+    #table = table[:rows] if rows else table 
+    result_tapas = pipe_tapas(table = table, query = query) 
+    return result_tapas['answer'], correct_answer 
+
+
+query_text = gr.Text(label = "Enter a question") 
+input_file = gr.File(label = "Upload a CVS file", type = "file") 
+
+# outputs from the app 
+answer_text_tapas = gr.Text(label = "TAPAS answer") 
+
+description = """
+# Siddharth Test Gradio Table QA
+"""
+
+iface = gr.Interface(
+    theme="huggingface",
+    description=description, 
+    fn = process, 
+    inputs = [query_text, input_file,], 
+    outputs = [answer_text_tapas],
+    examples = [["Apps with more than 4.7 rating in art and design?","playstore_text_csv.csv","Harley Quinn wallpapers HD --- ",],
+    ["How many apps have Beauty genres?","playstore_text_csv.csv",""],
+    ["Average Installs of apps with Beauty genres?","playstore_text_csv.csv",""] ]
+     ,
+    allow_flagging="never"
+)
+
+
+iface.launch(share=True)