Update app.py

app.py

@@ -1,63 +1,111 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import plotly.express as px
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Set environment variables for GPU usage and memory allocation
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+torch.cuda.empty_cache()
+torch.cuda.set_per_process_memory_fraction(0.8)  # Adjust the fraction as needed
+
+# Define device
+device = "cuda"  # The device to load the model onto
+
+# System message (placeholder, adjust as needed)
+system_message = ""
+
+# Load the model and tokenizer
+def hermes_model():
+    tokenizer = AutoTokenizer.from_pretrained("TheBloke/CapybaraHermes-2.5-Mistral-7B-AWQ")
+    model = AutoModelForCausalLM.from_pretrained(
+        "TheBloke/CapybaraHermes-2.5-Mistral-7B-AWQ", low_cpu_mem_usage=True, device_map="auto"
+    )
+    return model, tokenizer
+
+model, tokenizer = hermes_model()
+
+# Function to generate a response from the model
+def chat_response(msg_prompt: str) -> str:
+    """
+    Generates a response from the model given a prompt.
+
+    Args:
+        msg_prompt (str): The user's message prompt.
+
+    Returns:
+        str: The model's response.
+    """
+    generation_params = {
+        "do_sample": True,
+        "temperature": 0.7,
+        "top_p": 0.95,
+        "top_k": 40,
+        "max_new_tokens": 512,
+        "repetition_penalty": 1.1,
+    }
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, **generation_params)
+    try:
+        prompt_template = f'''system
+        {system_message}
+        user
+        {msg_prompt}
+        assistant
+        '''
+        pipe_output = pipe(prompt_template)[0]['generated_text']
+        
+        # Separate assistant's response from the output
+        response_lines = pipe_output.split('assistant')
+        assistant_response = response_lines[-1].strip() if len(response_lines) > 1 else pipe_output.strip()
+
+        return assistant_response
+    except Exception as e:
+        return str(e)
+
+# Function to generate a random plot
+def random_plot():
+    df = px.data.iris()
+    fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
+                     size='petal_length', hover_data=['petal_width'])
+    return fig
+
+# Function to handle likes/dislikes (for demonstration purposes)
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+# Function to add messages to the chat history
+def add_message(history, message):
+    for x in message["files"]:
+        history.append(((x,), None))
+    if message["text"] is not None:
+        history.append((message["text"], None))
+    return history, gr.update(value=None, interactive=True)
+
+# Function to simulate the bot response
+def bot(history):
+    user_message = history[-1][0]
+    bot_response = chat_response(user_message)
+    history[-1][1] = bot_response
+    return history
+
+fig = random_plot()
+
+# Gradio interface setup
+with gr.Blocks(fill_height=True) as demo:
+    chatbot = gr.Chatbot(elem_id="chatbot", bubble_full_width=False, scale=1)
+
+    chat_input = gr.MultimodalTextbox(
+        interactive=True,
+        file_count="multiple",
+        placeholder="Enter message or upload file...",
+        show_label=False
+    )
+
+    chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+    bot_msg = chat_msg.then(bot, chatbot, chatbot)
+    bot_msg.then(lambda: gr.update(interactive=True), None, [chat_input])
+
+    chatbot.like(print_like_dislike, None, None)
+
+demo.queue()
+demo.launch()