Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+import json
+
+context_val = ''
+
+q_n_a_model_name = "deepset/roberta-base-squad2"
+q_n_a_model = AutoModelForQuestionAnswering.from_pretrained(q_n_a_model_name)
+tokenizer = AutoTokenizer.from_pretrained(q_n_a_model_name)  # Corrected this line
+
+context = gr.Textbox(label="Add the Context (Paragraph or texts) for which you want to get insights", lines=10, outputs="text")
+
+def q_n_a_fn(context, text):
+    QA_input = {'question': text, 'context': context}
+    nlp = pipeline('question-answering', model=q_n_a_model, tokenizer=tokenizer)
+    res = nlp(QA_input)
+    answer = res['answer']
+    return answer
+
+def classification_fn(text):
+    return context
+
+def translate_fn(text):
+    return context
+    
+with gr.Blocks(theme='gradio/soft') as demo:
+    gr.Markdown("<h1>Basic NLP Operations</h1>")
+    gr.Markdown("Bringing basic NLP operations together.")
+    with gr.Tab("Question and Answer"):
+        with gr.Row():
+            gr.Interface(fn=q_n_a_fn, inputs=[context, gr.Textbox(label="Ask question", lines=1)], outputs="text")
+    
+    with gr.Tab("Classifier"):
+        with gr.Row():
+            gr.Interface(fn=classification_fn, inputs=[context], outputs="label")
+    
+    with gr.Tab("Translation"):
+        with gr.Row():
+            gr.Interface(fn=translate_fn, inputs=[gr.Radio(["French", "Hindi", "Spanish"], label="Languages", info="Select language")], outputs="text")
+
+    with gr.Tab("Summarization"):
+        with gr.Row():
+            gr.Interface(fn=classification_fn, inputs=[context], outputs="label")
+
+    with gr.Tab("Text To Speech"):
+        with gr.Row():
+            gr.Interface(fn=classification_fn, inputs=[context], outputs="audio")
+
+    with gr.Tab("Text To Text"):
+        with gr.Row():
+            gr.Interface(fn=classification_fn, inputs=[context], outputs="text")
+
+if __name__ == "__main__":
+    demo.launch()