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import gradio as gr |
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import os |
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from pathlib import Path |
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import argparse |
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from huggingface_hub import snapshot_download |
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repo_name = 'TheBloke/OpenHermes-2.5-Mistral-7B-GGUF' |
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model_file = "openhermes-2.5-mistral-7b.Q4_K_M.gguf" |
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print('Fetching model:', repo_name, model_file) |
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snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_file) |
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print('Done fetching model:') |
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DEFAULT_MODEL_PATH = model_file |
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from llama_cpp import Llama |
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llm = Llama(model_path=model_file, model_type="mistral") |
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isFirstRun = True |
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context = "" |
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history = [] |
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initContext = """<|im_start|>You are playing the role of an aggressive patient called Barry Lawson. Barry has been waiting for hours at the hospital with severe pain in his chest and hasn't been seen yet. You are incredibly frustrated and angry that things are taking so long, you will only calm down if your concerns are addressed. Waiting will only make you more angry and aggressive towards the staff. At any moment you could break into a RAGE! Make sure you don't censor swear words. Make sure to explain your emotional state and thinking before responding, for example, Barry: (tired of waiting and very angry) What the fuck do I have to do around here to get some treatment!""" |
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feedback_file = Path("/content/datalog.json") |
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def predict(input, chatbot, max_length, top_p, temperature, history): |
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chatbot.append((input, "")) |
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response = "" |
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history.append(input) |
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for output in llm(input, stream=True, temperature=temperature, top_p=top_p, max_tokens=max_length, ): |
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piece = output['choices'][0]['text'] |
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response += piece |
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chatbot[-1] = (chatbot[-1][0], response) |
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yield chatbot, history |
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history.append(response) |
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yield chatbot, history |
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def reset_user_input(): |
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return gr.update(value="") |
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def reset_state(): |
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return [], [] |
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def AIPatient(message): |
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global isFirstRun, history,context |
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if isFirstRun: |
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context = initContext |
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isFirstRun = False |
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context += """ |
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<|im_start|>nurse |
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Nurse: """+message+""" |
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<|im_start|>barry |
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Barry: |
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""" |
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response = "" |
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while(len(response) < 1): |
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print("here") |
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output = llm(context, max_tokens=400, stop=["Nurse:"], echo=False) |
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response = output["choices"][0]["text"] |
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response = response.strip() |
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context += response |
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print (context) |
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history.append((message,response)) |
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return history |
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with gr.Blocks() as demo: |
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gr.Markdown("# AI Patient Chatbot") |
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with gr.Group(): |
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with gr.Tab("Patient Chatbot"): |
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chatbot = gr.Chatbot() |
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message = gr.Textbox(label="Enter your message to Barry", placeholder="Type here...", lines=2) |
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send_message = gr.Button("Submit") |
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send_message.click(AIPatient, inputs=[message], outputs=[chatbot]) |
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save_chatlog = gr.Button("Save Chatlog") |
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demo.launch(debug=True,share=False,inbrowser=True) |
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