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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Intel/neural-chat-7b-v3-1") |
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model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-1") |
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def generate_response(system_input, user_input): |
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prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n" |
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inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False) |
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outputs = model.generate(inputs, max_length=1000, 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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return response.split("### Assistant:\n")[-1] |
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'''# Example usage |
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system_input = "Please act as a psychology counselor assistant. I will provide you counseling notes and you need to provide me with exactly two things in return, assessment and plan for the notes. Please note that the assessment should begin with keword 'Assessment:', and plan should begin with keyword 'Plan:'. Please make it logical, simple, concise, and clear." |
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user_input = "Can't sleep well?" |
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response = generate_response(system_input, user_input) |
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print(response) |
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''' |
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def generate_response1(user_input): |
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system_input = "Please act as a psychology counselor assistant. I will provide you counseling notes and you need to provide me with exactly two things in return, assessment and plan for the notes. Please note that the assessment should begin with keword 'Assessment:', and plan should begin with keyword 'Plan:'. Please make it logical, simple, concise, and clear." |
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prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n" |
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inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False) |
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outputs = model.generate(inputs, max_length=1000, 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_parts = response.split("### Assistant:\n")[-1] |
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assessment = response_parts.split("Plan:")[0].strip() |
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plan = "Plan:" + response_parts.split("Plan:")[1].strip() |
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return assessment, plan |
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import gradio as gr |
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iface = gr.Interface( |
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fn=generate_response1, |
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inputs=gr.Textbox(lines=2, label="Please Upload Notes here", placeholder="Enter your notes here..."), |
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outputs=[gr.Textbox(label="Assessment"), gr.Textbox(label="Plan")], |
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examples=[ |
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["Can't sleep well?"], |
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["Feeling anxious all the time."] |
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] |
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) |
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iface.launch(share=True) |
