Update TableQAGradio.py

TableQAGradio.py

@@ -1,143 +1,50 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# ## Using Gradio to create a simple interface.
-# 
-# Check out the library on [github](https://github.com/gradio-app/gradio-UI) and see the [getting started](https://gradio.app/getting_started.html) page for more demos.
-
-# We'll start with a basic function that greets an input name.
-
-# In[1]:
-
-
-# get_ipython().system('pip install -q gradio')
-
-
-# Now we'll wrap this function with a Gradio interface.
-
-# In[2]:
-
 
 from transformers import pipeline
 import pandas as pd
+import gradio as gr
 
-tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")
-
-
-# In[ ]:
-
-
-tsqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-sqa")
-
-
-# In[ ]:
-
-
-mstqa = pipeline(task="table-question-answering", model="microsoft/tapex-large-finetuned-wikisql")
-
-
-# In[ ]:
-
-
-mswtqa = pipeline(task="table-question-answering", model="microsoft/tapex-large-finetuned-wtq")
-
-
-# In[6]:
-
-
-# table2 = pd.read_excel("/content/Sample.xlsx").astype(str)
-# table3 = table2.head(20)
-
-
-# In[7]:
-
-
-# table3
-
-
-# In[ ]:
-
-
-#t4 = table3.reset_index()
-# table4
-
-
-# In[9]:
-
-
-query = "what is the highest delta onu rx power?"
-query2 = "what is the lowest delta onu rx power?"
-query3 = "what is the most frequent login id?"
-query4 = "how many rows with nan values are there?"
-query5 = "how many S2 values are there"
-
-
-# In[11]:
-
-
-# result = tsqa(table=table3, query=query5)["answer"]
-# result
-
-
-# In[13]:
-
-
-#mstqa(table=table4, query=query1)["answer"]
-
-
-# In[14]:
-
-
-# mswtqa(table=table3, query=query5)["answer"]
-
-
-# In[15]:
-
-
-def main(filepath, query):
-
-  table5 = pd.read_excel(filepath).head(20).astype(str)
-  result = tsqa(table=table5, query=query)["answer"]
-  return result
-
-#greet("World")
-
-
-# In[16]:
-
+# Define the models
+models = {
+    "GTQA (google/tapas-large-finetuned-wtq)": pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq"),
+    "GTSQA (google/tapas-large-finetuned-sqa)": pipeline(task="table-question-answering", model="google/tapas-large-finetuned-sqa"),
+    "MSWTQA (microsoft/tapex-large-finetuned-wtq)": pipeline(task="table-question-answering", model="microsoft/tapex-large-finetuned-wtq"),
+    "MSTQA (microsoft/tapex-large-finetuned-wikisql)": pipeline(task="table-question-answering", model="microsoft/tapex-large-finetuned-wikisql")
+}
+
+def main(model_choice, file_path, text):
+    # Read the Excel file
+    table_df = pd.read_excel(file_path).astype(str)
+    
+    # Prepare the input for the model
+    tqa_pipeline_input = {
+        "table": table_df,
+        "query": text
+    }
+    
+    # Get the selected model
+    model = models[model_choice]
+    
+    # Run the model
+    result = model(tqa_pipeline_input)["answer"]
+    return result
 
-import gradio as gr
 
 iface = gr.Interface(
     fn=main,
     inputs=[
+        gr.Dropdown(choices=list(models.keys()), label="Select Model"),
         gr.File(type="filepath", label="Upload XLSX file"),
         gr.Textbox(type="text", label="Enter text"),
     ],
     outputs=[gr.Textbox(type="text", label="Text Input Output")],
-    title="TM TableQA Test",
+    title="Multi-input Processor",
     description="Upload an XLSX file and/or enter text, and the processed output will be displayed.",
+    examples=[
+      ["https://huggingface.co/spaces/Abbasid/TableQA/blob/main/Literature%20review_Test.xlsx", "How many papers are before the year 2020?"],
+      ["https://huggingface.co/spaces/Abbasid/TableQA/blob/main/Literature%20review_Test.xlsx", "How many papers are after the year 2020?"],
+      ["https://huggingface.co/spaces/Abbasid/TableQA/blob/main/Literature%20review_Test.xlsx", "what is the paper with NISIT in the title?"],
+    ],
 )
 
 # Launch the Gradio interface
-iface.launch()
-
-
-# In[34]:
-
-
-import os
-import subprocess
-
-# Use subprocess to execute the shell command
-# subprocess.run(["jupyter", "nbconvert", "--to", "script", "--format", "script", "--output", "/content/", "/content/drive/MyDrive/Colab Notebooks/NEW TableQA-GRADIO: Hello World.ipynb"])
-
-
-# In[19]:
-
-
-# get_ipython().system('gradio deploy')
-
-
-
-# That's all! Go ahead and open that share link in a new tab. Check out our [getting started](https://gradio.app/getting_started.html) page for more complicated demos.
+iface.launch(debug=True)