Update app.py

app.py

@@ -1,16 +1,61 @@
 import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import time
+import numpy as np
+from torch.nn import functional as F
 import os
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+from threading import Thread
 token = os.environ["HF_TOKEN"]
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b",token=token)
-model = AutoModelForCausalLM.from_pretrained("google/gemma-2b",token=token)
-streamer = TextStreamer(tokenizer,skip_prompt=True)
 
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,token=token)
+tok = AutoTokenizer.from_pretrained("google/gemma-2b-it",token=token)
+# using CUDA for an optimal experience
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
 
-def generate(inputs,history):
-    inputs = tokenizer([inputs], return_tensors="pt")
-    yield model.generate(**inputs, streamer=streamer)
 
+start_message = ""
 
-app = gr.ChatInterface(generate)
-app.launch(debug=True)
+def user(message, history):
+    # Append the user's message to the conversation history
+    return "", history + [[message, ""]]
+
+
+def chat(message, history):
+    chat = []
+    for item in history:
+        chat.append({"role": "user", "content": item[0]})
+        if item[1] is not None:
+            chat.append({"role": "assistant", "content": item[1]})
+    chat.append({"role": "user", "content": message})
+    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    # Tokenize the messages string
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(
+        tok, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.95,
+        top_k=1000,
+        temperature=0.75,
+        num_beams=1,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    # Initialize an empty string to store the generated text
+    partial_text = ""
+    for new_text in streamer:
+        # print(new_text)
+        partial_text += new_text
+        # Yield an empty string to cleanup the message textbox and the updated conversation history
+        yield partial_text
+
+
+
+demo = gr.ChatInterface(fn=chat, examples=[["Write me a poem about Machine Learning."]], title="gemma 2b-it")
+demo.launch()