Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, GemmaTokenizerFast, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+# Load tokenizer and model
+tokenizer = GemmaTokenizerFast.from_pretrained("buddhist-nlp/gemma2-mitra-bo-instruct")
+model = AutoModelForCausalLM.from_pretrained("buddhist-nlp/gemma2-mitra-bo-instruct", torch_dtype=torch.float16).to('cuda:0')
+
+# Define custom stopping criteria
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        # Define stop tokens (adjust based on your model's tokenizer)
+        stop_ids = [29, 0]  # These should be the token IDs for end of response or similar tokens
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+# Define prediction function for the chat interface
+def predict(message, history):
+    # Prepare the conversation in the required format
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+
+    # Concatenate previous messages and the user's input
+    messages = "".join([f"\n### user : {item[0]} \n### bot : {item[1]}" for item in history_transformer_format])
+
+    # Tokenize the input
+    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
+    
+    # Set up the streamer for partial message output
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    
+    # Generate settings
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+    )
+    
+    # Run generation in a separate thread
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    # Stream partial messages as they are generated
+    partial_message = ""
+    for new_token in streamer:
+        if new_token != '<':  # Skip specific tokens if necessary
+            partial_message += new_token
+            yield partial_message
+
+# Create the chat interface using Gradio
+gr.ChatInterface(fn=predict, title="Gemma LLM Chatbot", description="Chat with the Gemma model using real-time generation and streaming.").launch(share=True)