add application file

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import os
+from threading import Thread
+import spaces
+import time
+import subprocess
+
+MIN_TOKENS=128
+MAX_TOKENS=8192
+DEFAULT_TOKENS=2048
+DURATION=60
+
+# Install flash attention
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+# Load model and tokenizer once when the app starts
+model_token = os.environ["HF_TOKEN"]
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-128k-instruct",
+    token=model_token,
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct", token=model_token)
+
+# Set device (GPU or CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define error handling function
+def handle_error(error):
+    return {"error": str(error)}
+
+# Define chat function with input validation and error handling
+@spaces.GPU(duration=DURATION)
+def chat(message, history, temperature, do_sample, max_tokens):
+    try:
+        # Validate input
+        if not message:
+            raise ValueError("Please enter a message")
+        if temperature < 0 or temperature > 1:
+            raise ValueError("Temperature must be between 0 and 1")
+        if max_tokens < MIN_TOKENS or max_tokens > MAX_TOKENS:
+            raise ValueError(f"Max tokens must be between {MIN_TOKENS} and {MAX_TOKENS}")
+
+        # Prepare chat 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})
+
+        # Generate response
+        messages = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+        model_inputs = tokenizer([messages], return_tensors="pt").to(device)
+        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
+        generate_kwargs = dict(
+            model_inputs,
+            streamer=streamer,
+            max_new_tokens=max_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            eos_token_id=[tokenizer.eos_token_id],
+        )
+
+        # Generate response
+        t = Thread(target=model.generate, kwargs=generate_kwargs)
+        t.start()
+
+        # Yield partial responses
+        partial_text = ""
+        for new_text in streamer:
+            partial_text += new_text
+            yield partial_text
+
+        # Yield final response
+        yield partial_text
+
+    except Exception as e:
+        yield handle_error(e)
+
+# Create Gradio interface
+demo = gr.ChatInterface(
+    fn=chat,
+    examples=[["Write me a poem about Machine Learning."]],
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
+    additional_inputs=[
+        gr.Slider(
+            minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
+        ),
+        gr.Checkbox(label="Sampling", value=True),
+        gr.Slider(
+            minimum=MIN_TOKENS,
+            maximum=MAX_TOKENS,
+            step=1,
+            value=DEFAULT_TOKENS,
+            label="Max new tokens",
+            render=False,
+        ),
+    ],
+    stop_btn="Stop Generation",
+    title="Chat With LLMs",
+    description="Now Running [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)",
+)
+
+# Launch Gradio app
+demo.launch()