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Browse files- app.py +44 -0
- requirements.txt +4 -0
app.py
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import gradio as gr
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from transformers import pipeline, RobertaTokenizer, RobertaForQuestionAnswering
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import torch
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# Load the model and tokenizer
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model_name = "AventIQ-AI/roberta-chatbot"
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tokenizer = RobertaTokenizer.from_pretrained(model_name)
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model = RobertaForQuestionAnswering.from_pretrained(model_name)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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# Initialize the question-answering pipeline
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qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
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# Define the function for the Gradio interface
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def roberta_chatbot(context, question):
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if not context or not question:
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return "Please provide both context and a question."
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# Get the model's answer
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result = qa_pipeline(question=question, context=context)
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answer = result.get('answer', 'Sorry, I could not find an answer.')
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return answer
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# Create the Gradio interface
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iface = gr.Interface(
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fn=roberta_chatbot,
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inputs=[
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gr.Textbox(label="π Context", placeholder="Enter the context here...", lines=5),
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gr.Textbox(label="β Question", placeholder="Enter your question here...", lines=2)
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],
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outputs=gr.Textbox(label="π€ Answer"),
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title="π§ RoBERTa-Powered Chatbot",
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description="Provide a context and ask a question. The RoBERTa-based chatbot will find the answer based on the given context.",
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examples=[
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["Flight AI101 departs from New York at 10:00 AM and arrives in San Francisco at 1:30 PM. The flight duration is 5 hours and 30 minutes.", "What is the duration of Flight AI101?"],
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["The Great Wall of China was built over several centuries to protect China's northern borders.", "Why was the Great Wall of China built?"]
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],
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theme="compact",
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allow_flagging="never"
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)
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if __name__ == "__main__":
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iface.launch()
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requirements.txt
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torch
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transformers
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gradio
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sentencepiece
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