Update README.md

README.md

@@ -60,7 +60,127 @@ This model is not meant as a decision support system in the wild, only for educa
 
 Use the code below to get started with the model.
 
-{{ get_started_code | default("[More Information Needed]", true)}}
+```python
+from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+from peft import PeftModel, PeftConfig
+import torch
+import gradio as gr
+import random
+from textwrap import wrap
+
+# 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:"):
+    """
+    Generates text using a large language model, given a user input and a system prompt.
+    Args:
+        user_input: The user's input text to generate a response for.
+        system_prompt: Optional system prompt.
+    Returns:
+        A string containing the generated text.
+    """
+    # Combine user input and system prompt
+    formatted_input = f"Question: {system_prompt} {user_input} \n Mini :"
+
+    # 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 = "mistralai/Mistral-7B-v0.1"
+model_directory = "Tonic/GaiaMiniMed"
+
+# 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.pad_token = tokenizer.eos_token
+tokenizer.padding_side = 'left'
+
+# Load the GaiaMiniMed model with the specified configuration
+
+peft_config = PeftConfig.from_pretrained("Tonic/GaiaMiniMed")
+peft_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b-instruct")
+peft_model = PeftModel.from_pretrained(model, "Tonic/GaiaMiniMed")
+
+# Specify the configuration class for the model
+#model_config = AutoConfig.from_pretrained(base_model_id)
+
+# Load the PEFT model with the specified configuration
+#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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")
+
+class ChatBot:
+    def __init__(self):
+        self.history = []
+
+class ChatBot:
+    def __init__(self):
+        # Initialize the ChatBot class with an empty history
+        self.history = []
+
+    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
+        # Combine the user's input with the system prompt
+        formatted_input = f"Question: {system_prompt} {user_input} Mini:"
+
+        # Encode the formatted input using the tokenizer
+        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
+
+        # Generate a response using the PEFT model
+        response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
+
+        # Decode the generated response to text
+        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+        
+        return response_text  # Return the generated response
+
+bot = ChatBot()
+
+title = "👋🏻Welcome to Tonic's GaiaMiniMed Chat🚀"
+description = "You can use this Space to test out the current model [(Tonic/GaiaMiniMed)](https://huggingface.co/Tonic/GaiaMiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
+examples = [["What is the proper treatment for buccal herpes?", "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]]
+
+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