create app

app.py

@@ -0,0 +1,137 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from peft import PeftModel
+import gradio as gr
+
+model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b")
+model = PeftModel.from_pretrained(model, "fadliaulawi/internlm-7b-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", padding_side="left", use_fast = False)
+
+def generate_prompt(
+    instruction, input, label
+):
+#   template = {
+#       "description": "Template used by Alpaca-LoRA.",
+#       "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
+#       "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
+#       "response_split": "### Response:"
+#   }
+# <s>[INST] <<SYS>>
+# {{ system_prompt }}
+# <</SYS>>
+
+# {{ user_message }} [/INST]
+#   return '''<s>[INST] <<SYS>>\n{0}\n<</SYS>>\n\n{1} {2} [/INST]'''.format(template['prompt_input'].format(instruction=instruction, input=input), template['response_split'], label)
+    template = {
+      "description": "Template used by Alpaca-LoRA.",
+      "prompt_input": "Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.\n\n### Instruksi:\n{instruction}\n\n### Masukan:\n{input}",
+      #"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
+      "response_split": "### Tanggapan:"
+    }
+
+    if input:
+      res = template["prompt_input"].format(instruction=instruction, input=input)
+    #else:
+    #  res = template["prompt_no_input"].format(instruction=instruction)
+
+    res = f"{res} \n\n### Tanggapan:\n"
+    if label:
+      res = f"{res}{label}"
+
+    return res
+
+def user(message, history):
+    return "", history + [[message, None]]
+
+def generate_and_tokenize_prompt(data_point):
+    full_prompt = generate_prompt(
+        data_point["instruction"],
+        data_point["input"],
+        data_point["output"],
+    )
+#     print(full_prompt)
+#     return
+    cutoff_len = 256
+    tokenizer.pad_token = tokenizer.eos_token
+    result = tokenizer(
+        full_prompt,
+        truncation=True,
+        max_length=cutoff_len,
+        padding=True,
+        return_tensors=None,
+    )
+
+    if (result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < cutoff_len):
+        result["input_ids"].append(tokenizer.eos_token_id)
+        result["attention_mask"].append(1)
+
+#     result["labels"] = result["input_ids"].copy()
+    return result
+
+def bot(history,temperature, max_new_tokens, top_p,top_k):
+    user_message = history[-1][0]
+    data = {
+        'instruction': "Jika Anda seorang dokter, silakan menjawab pertanyaan medis berdasarkan deskripsi pasien.",
+        'input': user_message,
+        'output': ''
+    }
+    
+    new_user_input_ids = generate_and_tokenize_prompt(data)
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.LongTensor([new_user_input_ids['input_ids']])
+
+    # generate a response
+    response = model.generate(
+        input_ids=bot_input_ids,
+        pad_token_id=tokenizer.eos_token_id,
+        temperature = float(temperature),
+        max_new_tokens=max_new_tokens,
+        top_p=float(top_p),
+        top_k=top_k,
+        do_sample=True
+    )
+
+    # clean up response before returning
+    response = tokenizer.batch_decode(response, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    sections = response.split("###")
+    response = sections[3]
+    response=response.split("Tanggapan:")[1].strip()
+    history[-1][1] = response
+    return history
+
+
+with gr.Blocks() as demo:
+  gr.Markdown(
+                """# ChatDoctor - InternLM 7b 🩺
+                
+                A [ChatDoctor - InternLM 7b](https://huggingface.co/fadliaulawi/internlm-7b-finetuned) demo.
+                From the [InternLM 7b](https://huggingface.co/internlm/internlm-7b) model and finetuned on the Indonesian translation of [ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor) dataset.
+                
+                """
+            )
+  
+  chatbot = gr.Chatbot()
+  msg = gr.Textbox()
+  submit = gr.Button("Submit")
+  clear = gr.Button("Clear")
+  examples = gr.Examples(examples=["Dokter, aku mengalami kelelahan akhir-akhir ini.", "Dokter, aku merasa pusing, lemah dan sakit dada tajam akhir-akhir ini.",
+                                       "Dokter, aku merasa sangat depresi akhir-akhir ini dan juga mengalami perubahan suhu tubuhku.", 
+                                       "Dokter, saya sudah beberapa minggu mengalami suara serak dan tidak kunjung membaik meski sudah minum obat. Apa masalahnya?"
+                                       ],inputs=[msg])
+  
+  gr.Markdown(
+                """## Adjust the additional inputs:"""
+            )
+  
+  temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature',info="Controls randomness, higher values increase diversity.")
+  max_length = gr.Slider(0, 1024, value=50, step=1, label='Max Length',info="The maximum numbers of output's tokens.")
+  top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P',info="The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.")
+  top_k = gr.Slider(0, 50, value=10, step=1, label='Top K',info="Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens.")
+
+  submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, [chatbot,temperature,max_length,top_p,top_k], chatbot
+    )
+  clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue(concurrency_count=100).launch()