Update app.py

app.py

@@ -7,6 +7,9 @@ from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoM
 from peft import PeftModel, PeftConfig
 import torch
 import gradio as gr
+import os
+
+hf_token = os.environ.get('HUGGINGFACE_TOKEN')
 
 # Functions to Wrap the Prompt Correctly
 def wrap_text(text, width=90):
@@ -14,7 +17,6 @@ def wrap_text(text, width=90):
     wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
     wrapped_text = '\n'.join(wrapped_lines)
     return wrapped_text
-
 def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):
     """
     Generates text using a large language model, given a user input and a system prompt.
@@ -25,7 +27,7 @@ def multimodal_prompt(user_input, system_prompt="You are an expert medical analy
         A string containing the generated text.
     """
     # Combine user input and system prompt
-    formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
+    formatted_input = f"[INSTRUCTION]{system_prompt} {user_input}"
 
     # Encode the input text
     encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
@@ -53,12 +55,12 @@ def multimodal_prompt(user_input, system_prompt="You are an expert medical analy
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Use the base model's ID
-base_model_id = "mistralai/Mistral-7B-v0.1"
-model_directory = "Tonic/mistralmed"
+base_model_id = "stabilityai/stablelm-3b-4e1t"
+model_directory = "Tonic/stablemed"
 
 # Instantiate the Tokenizer
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
-# tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", trust_remote_code=True, padding_side="left")
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", trust_remote_code=True, padding_side="left")
+# tokenizer = AutoTokenizer.from_pretrained("Tonic/stablemed", trust_remote_code=True, padding_side="left")
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.padding_side = 'left'
 
@@ -69,9 +71,9 @@ tokenizer.padding_side = 'left'
 #peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config)
 
 # Load the PEFT model
-peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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")
-peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
-peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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")
+peft_config = PeftConfig.from_pretrained("Tonic/stablemed", token=hf_token)
+peft_model = MistralForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", trust_remote_code=True)
+peft_model = PeftModel.from_pretrained(peft_model, "Tonic/stablemed", token=hf_token)
 
 class ChatBot:
     def __init__(self):
@@ -79,7 +81,7 @@ class ChatBot:
 
     def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
         # Combine user input and system prompt
-        formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
+        formatted_input = f"[INSTRUCTION:]{system_prompt}[QUESTION:] {user_input}"
 
         # Encode user input
         user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
@@ -91,7 +93,7 @@ class ChatBot:
             chat_history_ids = user_input_ids
 
         # Generate a response using the PEFT model
-        response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
+        response = peft_model.generate(input_ids=chat_history_ids, max_length=400, pad_token_id=tokenizer.eos_token_id)
 
         # Update chat history
         self.history = chat_history_ids
@@ -102,8 +104,8 @@ class ChatBot:
 
 bot = ChatBot()
 
-title = "👋🏻Welcome to Tonic's MistralMed Chat🚀"
-description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together."
+title = "👋🏻Welcome to Tonic's StableMed Chat🚀"
+description = "You can use this Space to test out the current model [StableMed](https://huggingface.co/Tonic/stablemed) or You can also use 😷StableMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/StableMed_Chat?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> #### Join us : 🌟TeamTonic�� is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"             "
 examples = [["What is the proper treatment for buccal herpes?", "Please provide information on the most effective antiviral medications and home remedies for treating buccal herpes."]]
 
 iface = gr.Interface(