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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "aubmindlab/aragpt2-base" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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def generate_response(user_input): |
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prompt = f"مستخدم: {user_input}\nنظام الدعم النفسي:" |
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inputs = tokenizer.encode(prompt, return_tensors="pt") |
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outputs = model.generate(inputs, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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response = response.split("نظام الدعم النفسي:")[-1].strip() |
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return response |
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with gr.Blocks() as app: |
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gr.Markdown("# تطبيق الدعم النفسي") |
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gr.Markdown("اكتب مشكلتك هنا وسنساعدك بأفضل طريقة ممكنة.") |
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with gr.Row(): |
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input_text = gr.Textbox(label="اكتب مشكلتك هنا", placeholder="مثال: أشعر بالقلق الشديد...") |
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output_text = gr.Textbox(label="الرد") |
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submit_button = gr.Button("أرسل") |
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submit_button.click(generate_response, inputs=input_text, outputs=output_text) |
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app.launch() |
