Implement chatbot

app.py

@@ -1,7 +1,75 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import os
+from huggingface_hub import login
+import spaces
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+token = os.environ.get("HF_TOKEN_READ_LLAMA")
+login(token)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+model_name = 'meta-llama/Meta-Llama-3.1-8B-Instruct'
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype = torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(madel_name)
+
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+else:
+    device = torch.device('cpu')
+
+model = model.to(device)
+
+@spaces.GPU
+def response(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+
+    for value in history:
+        if value[0]:
+            messages.append({"role": "user", "content": value[0]})
+        if value[1]:
+            messages.append({"role": "assistant", "content": value[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors='pt'
+    ).to(model.device)
+
+    terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=max_tokens,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p
+    )
+
+    response = ''
+
+    for message in tokenizer.decode(
+        outputs[0][input_ids.shape[-1]:],
+        skip_special_tokens=True
+    ):
+        response += message
+        yield response
+
+
+demo = gr.ChatInterface(
+    response,
+    additional_inputs = [
+        gr.Textbox(value="You are a friendly assistant", label="System Message"),
+        gr.Slider(minimum=1, maximun=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximun=4, value=0.7, step=0.1, label="Temperature"),   
+        gr.Slider(minimum=0.1, maximun=1, value=0.9, step=0.05, label="Top_p"),    
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+transformers
+torch
+huggingface_hub
+spaces
+gradio