Update app.py

app.py

@@ -1,5 +1,5 @@
 import os
-import streamlit as st
+import gradio as gr
 from huggingface_hub import HfApi, SpaceHardware
 
 # Set up Hugging Face API token and Space ID
@@ -17,44 +17,60 @@ def get_task():
 # 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!")
+    return 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!")
+    return 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}")
+    return f"Training model with task: {task}"
 
-# Check if there’s an existing task
-task = get_task()
+# Gradio function to simulate chat-like interface
+def gradio_fn(task_input, history):
+    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)
+    if task is None:
+        # If no task, add a new task and request hardware
+        add_task_response = add_task(task_input)
         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)
+        
+        # Add the new task response to the chat history
+        history.append(("Bot", add_task_response))
+        return "", history  # Clear the input box and return updated history
     else:
-        # If GPU hardware is not available, request it
-        api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)
+        # 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_response = train_and_upload(task)
+            mark_as_done_response = mark_as_done(task)
+            
+            # Add responses to history
+            history.append(("Bot", train_and_upload_response))
+            history.append(("Bot", mark_as_done_response))
+            
+            # 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)
+            history.append(("Bot", "Requesting GPU hardware..."))
+        
+        return "", history  # Clear the input box and return updated history
+
+# Create the Gradio interface for chat
+chat_interface = gr.Interface(
+    fn=gradio_fn,
+    inputs=[gr.Textbox(label="Enter task name", placeholder="Type your task here...", lines=1)],
+    outputs=[gr.Chatbot()],
+    live=True,
+    title="Task Manager Bot",  # Optional: Title for the interface
+    description="Interact with the bot to manage tasks and trigger model training."
+)
+
+# Launch the Gradio interface
+chat_interface.launch()