Update app.py

app.py

@@ -1,67 +1,60 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Initialize the Inference Client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    system_message = system_message or "You are a friendly Python code writing Chatbot. Assist with Python programming tasks, debugging, and code optimization. Provide solutions for Python-related queries, help with libraries, algorithms, and best practices, and generate clean, efficient code for various applications."
-    
-    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
-
-
-def clear_chat():
-    return [], ""
-
-# Chatbot Interface
-demo = gr.Interface(
-    fn=respond,
-    inputs=[
-        gr.Textbox(value="You are a friendly Python code writing Chatbot. Assist with Python programming tasks, debugging, and code optimization. Provide solutions for Python-related queries, help with libraries, algorithms, and best practices, and generate clean, efficient code for various applications.", label="System message"),
-        gr.Textbox(placeholder="Enter your message here...", label="User Input", lines=2),
-        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)"),
-    ],
-    outputs=[
-        gr.Chatbot(label="Chat History"),
-        gr.Textbox(label="Current Response", placeholder="Chatbot will reply here...", interactive=False)
-    ],
-    live=True,
-    allow_flagging="never",  # Disable flagging
-    layout="vertical",
-    theme="huggingface",  # Optionally, set a different theme
-)
-
-# Add additional features to the interface
-with demo:
-    gr.Button("Clear Chat", elem_id="clear-chat").click(clear_chat, outputs=["chatbot", "textbox"])
-
-if __name__ == "__main__":
-    demo.launch()
+import os
+import streamlit as st
+from huggingface_hub import HfApi, SpaceHardware
+
+# Set up Hugging Face API token and Space ID
+HF_TOKEN = os.getenv("HF_TOKEN")  # Ensure your Hugging Face token is set as a secret
+TRAINING_SPACE_ID = "your_space_id_here"  # Replace with your actual space ID
+
+# Initialize Hugging Face API
+api = HfApi(token=HF_TOKEN)
+
+# Function to check for a scheduled task (this is a placeholder for your actual task-checking logic)
+def get_task():
+    # You can implement logic here to check for scheduled tasks
+    return None  # For example, return None if no task is scheduled
+
+# Function to add a new task (you can implement this depending on your use case)
+def add_task(task):
+    # Logic to add a new task
+    st.write(f"Task '{task}' added!")
+
+# Function to mark the task as "DONE" (this is a placeholder)
+def mark_as_done(task):
+    # Mark the task as done once it's completed
+    st.write(f"Task '{task}' completed!")
+
+# Function to simulate training the model (replace with actual training logic)
+def train_and_upload(task):
+    # Implement your model training logic here
+    st.write(f"Training model with task: {task}")
+
+# Check if there’s an existing task
+task = get_task()
+
+if task is None:
+    # Display Gradio interface to request a new task
+    def gradio_fn(task):
+        # On user request, add task and request hardware
+        add_task(task)
+        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)
+
+    # Use Streamlit to request a task (Gradio interface or a simple button to simulate this)
+    task_input = st.text_input("Enter task name", "")
+    if st.button("Request Task"):
+        gradio_fn(task_input)
+else:
+    # If a task is available, check for hardware
+    runtime = api.get_space_runtime(repo_id=TRAINING_SPACE_ID)
+    if runtime.hardware == SpaceHardware.T4_MEDIUM:
+        # Fine-tune model on GPU if available
+        train_and_upload(task)
+
+        # Mark task as "DONE" after training
+        mark_as_done(task)
+
+        # Reset to CPU hardware after training
+        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.CPU_BASIC)
+    else:
+        # If GPU hardware is not available, request it
+        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)