Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""ml-app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1d6MSh5DMmENGXOTWdwsTlwgIqzphq4Pu
+"""
+
+!pip install gradio
+
+import gradio as gr
+from transformers import pipeline
+import re
+import nltk
+nltk.download('punkt')
+
+!pip install unidecode
+
+from unidecode import unidecode
+
+# 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()
+