Remove excessive lines

Main&Gradio-huggingface.py

@@ -1,72 +1,70 @@
-import transformers
-import torch
-import gradio as gr
-from datasets import load_dataset
-
-# Load the model once when the script starts
-model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# Load the model into memory (on GPU if available)
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",  # Auto-detect GPU
-)
-
-# Load the dataset from Hugging Face
-dataset = load_dataset("quantumminds/cisco_cli_commands")
-
-# Function to search the dataset for a matching command
-def search_dataset(user_input):
-    # Check if any command in the dataset matches the user input
-    for entry in dataset['train']:  # assuming the dataset is in the 'train' split
-        if entry["command"].lower() in user_input.lower():  # Match the command with user input (case-insensitive)
-            return f"**Command:** {entry['command']}\n\n**Description:** {entry['description']}\n\n**Example:** {entry['examples'][0]['example_command'] if 'examples' in entry else 'No example available'}"
-    return None  # If no match found
-
-# Function to generate response using the dataset or fallback to the pipeline
-def generate_response(user_input, chat_history):
-    # First, try to find a match in the dataset
-    dataset_response = search_dataset(user_input)
-    
-    if dataset_response:
-        # Add user and assistant responses to the chat history
-        chat_history.append({"role": "user", "content": user_input})
-        chat_history.append({"role": "assistant", "content": "You are a heplfull chatbot who specializes in Cisco switch and router configurations" + assistant_response})
-        return chat_history
-
-    # Generate the response from the LLM
-    outputs = pipeline(user_input, max_new_tokens=512)
-    
-    # Generate the assistant's response
-    assistant_response = outputs[0]["generated_text"]
-    
-    # Add user and assistant responses to the chat history
-    chat_history.append({"role": "user", "content": user_input})
-    chat_history.append({"role": "assistant", "content": assistant_response})
-
-    return chat_history
-
-# Create Gradio interface with chatbot and textbox
-with gr.Blocks(theme=gr.themes.Ocean()) as iface:
-    gr.Markdown("<h1 style='text-align: center;'>Cisco Configuration Assistant</h1>")
-    chatbot = gr.Chatbot(label="Cisco Configuration Chatbot", type="messages", height=500)
-    user_input = gr.Textbox(placeholder="Enter your Cisco switch/router question here...", label="Your Input")
-    with gr.Row():
-        submit_btn = gr.Button("Submit")
-        clear_btn = gr.Button("Clear Feed")
-    
-    def user(query, history):
-        # Generate a response and update the history
-        history = generate_response(query, history)
-        return history, ""  # Return updated history and clear the input box
-    # Submit user input and update the chat history
-    user_input.submit(user, [user_input, chatbot], [chatbot, user_input])
-    submit_btn.click(user, [user_input, chatbot], [chatbot, user_input])
-    clear_btn.click(lambda: [], None, chatbot, queue=False)
-
-# Launch the Gradio app
-iface.launch()
+import transformers
+import torch
+import gradio as gr
+from datasets import load_dataset
+
+# Load the model once when the script starts
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+# Load the model into memory (on GPU if available)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",  # Auto-detect GPU
+)
+
+# Load the dataset from Hugging Face
+dataset = load_dataset("quantumminds/cisco_cli_commands")
+
+# Function to search the dataset for a matching command
+def search_dataset(user_input):
+    # Check if any command in the dataset matches the user input
+    for entry in dataset['train']:  # assuming the dataset is in the 'train' split
+        if entry["command"].lower() in user_input.lower():  # Match the command with user input (case-insensitive)
+            return f"**Command:** {entry['command']}\n\n**Description:** {entry['description']}\n\n**Example:** {entry['examples'][0]['example_command'] if 'examples' in entry else 'No example available'}"
+    return None  # If no match found
+
+# Function to generate response using the dataset or fallback to the pipeline
+def generate_response(user_input, chat_history):
+    # First, try to find a match in the dataset
+    dataset_response = search_dataset(user_input)
+    
+    if dataset_response:
+        # Add user and assistant responses to the chat history
+        chat_history.append({"role": "user", "content": user_input})
+        chat_history.append({"role": "assistant", "content": "You are a heplfull chatbot who specializes in Cisco switch and router configurations" + assistant_response})
+        return chat_history
+
+    # Generate the response from the LLM
+    outputs = pipeline(user_input, max_new_tokens=512)
+    
+    # Generate the assistant's response
+    assistant_response = outputs[0]["generated_text"]
+    
+    # Add user and assistant responses to the chat history
+    chat_history.append({"role": "user", "content": user_input})
+    chat_history.append({"role": "assistant", "content": assistant_response})
+
+    return chat_history
+
+# Create Gradio interface with chatbot and textbox
+with gr.Blocks(theme=gr.themes.Ocean()) as iface:
+    gr.Markdown("<h1 style='text-align: center;'>Cisco Configuration Assistant</h1>")
+    chatbot = gr.Chatbot(label="Cisco Configuration Chatbot", type="messages", height=500)
+    user_input = gr.Textbox(placeholder="Enter your Cisco switch/router question here...", label="Your Input")
+    with gr.Row():
+        submit_btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear Feed")
+    
+    def user(query, history):
+        # Generate a response and update the history
+        history = generate_response(query, history)
+        return history, ""  # Return updated history and clear the input box
+    # Submit user input and update the chat history
+    user_input.submit(user, [user_input, chatbot], [chatbot, user_input])
+    submit_btn.click(user, [user_input, chatbot], [chatbot, user_input])
+    clear_btn.click(lambda: [], None, chatbot, queue=False)
+
+# Launch the Gradio app
+iface.launch()