Create app.py

app.py

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+!pip install gradio transformers langchain -Uqqq
+!pip install accelerate bitsandbytes einops git+https://github.com/huggingface/peft.git -Uqqq
+
+
+import gradio as gr
+import torch
+import re, os, warnings
+from langchain import PromptTemplate, LLMChain
+from langchain.llms.base import LLM
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig
+from peft import LoraConfig, get_peft_model, PeftConfig, PeftModel
+warnings.filterwarnings("ignore")
+
+# initialize and load PEFT model and tokenizer
+def init_model_and_tokenizer(PEFT_MODEL):
+  config = PeftConfig.from_pretrained(PEFT_MODEL)
+  bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.float16,
+  )
+
+  peft_base_model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+  )
+
+  peft_model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)
+
+  peft_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+  peft_tokenizer.pad_token = peft_tokenizer.eos_token
+
+  return peft_model, peft_tokenizer
+
+
+# custom LLM chain to generate answer from PEFT model for each query
+def init_llm_chain(peft_model, peft_tokenizer):
+    class CustomLLM(LLM):
+        def _call(self, prompt: str, stop=None, run_manager=None) -> str:
+            device = "cuda:0"
+            peft_encoding = peft_tokenizer(prompt, return_tensors="pt").to(device)
+            peft_outputs = peft_model.generate(input_ids=peft_encoding.input_ids, generation_config=GenerationConfig(max_new_tokens=256, pad_token_id = peft_tokenizer.eos_token_id, \
+                                                                                                                     eos_token_id = peft_tokenizer.eos_token_id, attention_mask = peft_encoding.attention_mask, \
+                                                                                                                     temperature=0.4, top_p=0.6, repetition_penalty=1.3, num_return_sequences=1,))
+            peft_text_output = peft_tokenizer.decode(peft_outputs[0], skip_special_tokens=True)
+            return peft_text_output
+
+        @property
+        def _llm_type(self) -> str:
+            return "custom"
+
+    llm = CustomLLM()
+
+    template = """Answer the following question truthfully.
+    If you don't know the answer, respond 'Sorry, I don't know the answer to this question.'.
+    If the question is too complex, respond 'Kindly, consult a psychiatrist for further queries.'.
+
+    Example Format:
+    : question here
+    : answer here
+
+    Begin!
+
+    : {query}
+    :"""
+
+    prompt = PromptTemplate(template=template, input_variables=["query"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+    return llm_chain
+
+def user(user_message, history):
+  return "", history + [[user_message, None]]
+
+def bot(history):
+  if len(history) >= 2:
+    query = history[-2][0] + "\n" + history[-2][1] + "\nHere, is the next QUESTION: " + history[-1][0]
+  else:
+    query = history[-1][0]
+
+  bot_message = llm_chain.run(query)
+  bot_message = post_process_chat(bot_message)
+
+  history[-1][1] = ""
+  history[-1][1] += bot_message
+  return history
+
+def post_process_chat(bot_message):
+  try:
+    bot_message = re.findall(r":.*?Begin!", bot_message, re.DOTALL)[1]
+  except IndexError:
+    pass
+
+  bot_message = re.split(r'\:?\s?', bot_message)[-1].split("Begin!")[0]
+
+  bot_message = re.sub(r"^(.*?\.)(?=\n|$)", r"\1", bot_message, flags=re.DOTALL)
+  try:
+    bot_message = re.search(r"(.*\.)", bot_message, re.DOTALL).group(1)
+  except AttributeError:
+    pass
+
+  bot_message = re.sub(r"\n\d.$", "", bot_message)
+  bot_message = re.split(r"(Goodbye|Take care|Best Wishes)", bot_message, flags=re.IGNORECASE)[0].strip()
+  bot_message = bot_message.replace("\n\n", "\n")
+
+  return bot_message
+    
+model = "heliosbrahma/falcon-7b-sharded-bf16-finetuned-mental-health-conversational"
+peft_model, peft_tokenizer = init_model_and_tokenizer(PEFT_MODEL = model)
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""Welcome to Mental Health Conversational AI""")
+    gr.Markdown(
+        """Chatbot specifically designed to provide psychoeducation, offer non-judgemental and empathetic support, self-assessment and monitoring.
+        Get instant response for any mental health related queries. If the chatbot seems you need external support, then it will respond appropriately."""
+    )
+
+    chatbot = gr.Chatbot()
+    query = gr.Textbox(label="Type your query here, then press 'enter' and scroll up for response")
+    clear = gr.Button(value="Clear Chat History!")
+    clear.style(size="sm")
+
+    llm_chain = init_llm_chain(peft_model, peft_tokenizer)
+
+    query.submit(user, [query, chatbot], [query, chatbot], queue=False).then(bot, chatbot, chatbot)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue().launch(inline=False)