Update app.py

app.py

@@ -17,6 +17,8 @@ demo.launch()
 #!pip install accelerate
 #!pip install -i 
 
+"""
+
 import gradio as gr
 import torch
 from peft import PeftModel, PeftConfig
@@ -40,8 +42,8 @@ newmodel = PeftModel.from_pretrained(newmodel, peft_model_id,
                                     use_auth_token="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", load_in_8bit=True, device_map='auto')
 
 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_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")
@@ -56,4 +58,37 @@ def mental_chat(message, history):
 
 demo = gr.ChatInterface(mental_chat)
 
-demo.launch()
+demo.launch()
+
+"""
+
+
+import gradio as gr
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
+
+# Load the Lora model
+newmodel = PeftModel.from_pretrained(peft_model_id, use_auth_token="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")
+
+newtokenizer = AutoTokenizer.from_pretrained(peft_model_id, use_auth_token="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")
+
+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)
+
+def mental_chat(message, history):
+    return givetext(message, newmodel, newtokenizer)
+
+demo = gr.ChatInterface(mental_chat)
+
+demo.launch()