Delete app.py

app.py

@@ -1,49 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import re
-from unidecode import unidecode
-import sentencepiece
-import nltk
-nltk.data.path.append('./nltk_data')
-
-# Preprocessing function: lowercasing and removing special characters
-def preprocess_text(text):
-    text = text.lower()
-    text = unidecode(text)
-    text = re.sub(r'\W+', ' ', text)  # Remove non-word characters
-    tokens = nltk.word_tokenize(text)
-    return ' '.join(tokens)  # Returning as a single string instead of tokens for the context
-
-# Load the multilingual model for question answering
-qa_model = pipeline("question-answering", model="deepset/xlm-roberta-large-squad2")
-
-# Function to generate the answer based on question and uploaded context
-def answer_question(question, context):
-    try:
-        preprocessed_context = preprocess_text(context)
-        result = qa_model(question=question, context=preprocessed_context)
-        return result['answer']
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Gradio interface
-def qa_app(text_file, question):
-    try:
-        with open(text_file.name, 'r') as file:
-            context = file.read()
-        return answer_question(question, context)
-    except Exception as e:
-        return f"Error reading file: {str(e)}"
-
-# Create Gradio interface with updated syntax
-iface = gr.Interface(
-    fn=qa_app,  # The function that processes input
-    inputs=[gr.File(label="Upload your text file"), gr.Textbox(label="Enter your question")],
-    outputs="text",
-    title="Multilingual Question Answering",
-    description="Upload a text file and ask a question based on its content."
-)
-
-# Launch the Gradio app
-iface.launch()
-