Update README.md

README.md

@@ -36,7 +36,7 @@ specifically to improve our understanding of outreach and communication.
 ### Model Sources [optional]
 
 - **Repository:** [Tonic/stablemed](https://huggingface.co/Tonic/stablemed)
-- **Demo [optional]:** [More Information Needed]
+- **Demo :** [Tonic/StableMed_Chat](https://huggingface.co/Tonic/StableMed_Chat)
 
 ## Uses
 
@@ -83,7 +83,114 @@ DO NOT USE THIS MODEL WITHOUT FURTHER FINETUNING
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```Python
+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
+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"[INSTRUCTION]{system_prompt}[QUESTION]{user_input}"
+
+    # 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", 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 = 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):
+        self.history = []
+
+    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
+        # Combine user input and system prompt
+        formatted_input = f"[INSTRUCTION:]{system_prompt}[QUESTION:] {user_input}"
+
+        # 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=400, pad_token_id=tokenizer.eos_token_id)
+
+        # Update chat history
+        self.history = chat_history_ids
+
+        # Decode and return the response
+        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+        return response_text
+
+bot = ChatBot()
+
+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. 🧬🔬🔍 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(
+    fn=bot.predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs=["text", "text"],  # Take user input and system prompt separately
+    outputs="text",
+    theme="ParityError/Anime"
+)
+iface.launch()
+```
 
 ## Training Details