Update app.py

app.py

@@ -1,100 +1,53 @@
-from transformers import AutoTokenizer, MistralForCausalLM
-import torch
-import gradio as gr
-import random
-from textwrap import wrap
-from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
-from peft import PeftModel, PeftConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 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):
-    lines = text.split('\n')
-    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:"):
-
-    # Combine user input and system prompt
-    formatted_input = f"{user_input}{system_prompt}"
-
-    # Encode the input text
-    encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
-    model_inputs = encodeds.to(device)
-
-    # Generate a response using the model
-    output = model.generate(
-        **model_inputs,
-        max_length=max_length,
-        use_cache=True,
-        early_stopping=True,
-        bos_token_id=model.config.bos_token_id,
-        eos_token_id=model.config.eos_token_id,
-        pad_token_id=model.config.eos_token_id,
-        temperature=0.1,
-        do_sample=True
-    )
-
-    # Decode the response
-    response_text = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    return response_text
-
 # Define the device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Use the base model's ID
-base_model_id = "stabilityai/stablelm-3b-4e1t"
-model_directory = "Tonic/stablemed"
-
-# Instantiate the Tokenizer
-tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, 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'
-
-# Load the PEFT model
-peft_config = PeftConfig.from_pretrained("Tonic/stablemed", token=hf_token)
-peft_model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True)
-peft_model = PeftModel.from_pretrained(peft_model, "Tonic/stablemed", token=hf_token)
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b', token=hf_token)
+model = AutoModelForCausalLM.from_pretrained(
+    'stabilityai/stablelm-zephyr-3b',
+    trust_remote_code=True,
+    device_map="auto",
+    token=hf_token
+)
+model.to(device)
 
 class ChatBot:
     def __init__(self):
         self.history = []
 
     def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
-        # Combine user input and system prompt
-        formatted_input = f"{user_input}{system_prompt}"
-
-        # Encode user input
-        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
-
-        # Concatenate the user input with chat history
-        if len(self.history) > 0:
-            chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1)
-        else:
-            chat_history_ids = user_input_ids
-
-        # Generate a response using the PEFT model
-        response = peft_model.generate(input_ids=chat_history_ids, max_length=1200, pad_token_id=tokenizer.eos_token_id)
-
-        # Update chat history
-        self.history = chat_history_ids
+        prompt = [{'role': 'user', 'content': user_input}, {'role': 'system', 'content': system_prompt}]
+        inputs = tokenizer.apply_chat_template(
+            prompt,
+            add_generation_prompt=True,
+            return_tensors='pt'
+        )
+
+        # Generate a response using the model
+        tokens = model.generate(
+            inputs.to(model.device),
+            max_new_tokens=1024,
+            temperature=0.8,
+            do_sample=True
+        )
 
         # Decode and return the response
-        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+        response_text = tokenizer.decode(tokens[0], skip_special_tokens=False)
         return response_text
 
 bot = ChatBot()
 
-title = "👋🏻Welcome to Tonic's 😷StableMed⚕️ Chat🦟"
+title = "👋🏻Welcome to 🌟Tonic's🗽Stable🌟LM 3B🚀Chat"
 description = """
-You can use this Space to test out the current model [StableMed](https://huggingface.co/Tonic/stablemed)
-You can also use 😷StableMed⚕️ on your laptop & 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> 
+You can use this Space to test out the current model [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b)
+You can also use 😷StableMed⚕️ on your laptop & by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/TonicsStableLM3B?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."]]