Create app.py

app.py

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+from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+import torch
+import gradio as gr
+import random
+from textwrap import wrap
+import spaces
+
+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
+
+@spaces.GPU
+def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):
+    # Combine user input and system prompt
+    formatted_input = f"<s> [INST] {example_instruction} [/INST] {example_answer}</s> [INST] {system_prompt} [/INST]"
+
+    # 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
+model_id = "SuperAGI/SAM"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id = model_id, trust_remote_code=True)
+# tokenizer.pad_token = tokenizer.eos_token
+# tokenizer.padding_side = 'left'
+
+# Specify the configuration class for the model
+#model_config = AutoConfig.from_pretrained(base_model_id)
+
+model = MistralForCaumodel = AutoModelForCausalLM.from_pretrained(model_id)
+
+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"<s>[INST]{system_prompt} {user_input}[/INST]"
+
+        # 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🤖SuperAGI/SAM Chat🚀"
+description = "SAM is an Agentic-Native LLM that excels at complex reasoning. You can use this Space to test out the current model [Tonic/superagi-sam](https://huggingface.co/Tonic/superagi-sam) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
+examples = [["[Question:] 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"]]
+
+
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown(title)
+        gr.Markdown(description)
+        with gr.Row():
+            with gr.Column():
+                example_instruction = gr.Textbox(label="Example Instruction")
+                example_answer = gr.Textbox(label="Example Answer")
+            with gr.Column():
+                user_input = gr.Textbox(label="Your Question")
+                system_prompt = gr.Textbox(label="System Prompt", value="You are an expert medical analyst:")
+        submit_btn = gr.Button("Submit")
+        output = gr.Textbox(label="Response")
+
+        submit_btn.click(
+            fn=bot.predict,
+            inputs=[example_instruction, example_answer, user_input, system_prompt],
+            outputs=output
+        )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()