Update app.py

app.py

@@ -1,3 +1,4 @@
+"""
 import gradio as gr
 
 def mental_chat(message, history):
@@ -5,4 +6,32 @@ def mental_chat(message, history):
 
 demo = gr.ChatInterface(mental_chat)
 
+demo.launch()
+"""
+
+peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
+config = PeftConfig.from_pretrained(peft_model_id)
+newmodel = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+newtokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+newmodel = PeftModel.from_pretrained(newmodel, peft_model_id)
+
+def givetext(input_text,lmodel,ltokenizer):
+  eval_prompt_pt1 = """\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: """
+  eval_prompt_pt2="""\n\n\n### Response:\n"""
+  eval_prompt=eval_prompt_pt1+input_text+eval_prompt_pt2
+  print(eval_prompt,"\n\n")
+  model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cuda")
+
+  lmodel.eval()
+  with torch.no_grad():
+    return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True))
+    #return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
+
+def mental_chat(message, history):
+    return givetext(patienttext,newmodel,newtokenizer)
+
+demo = gr.ChatInterface(mental_chat)
+
 demo.launch()