Delete Main&Gradio.py

Main&Gradio.py

@@ -1,70 +0,0 @@
-import transformers
-import torch
-import gradio as gr
-import json
-
-# 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 the local JSON file
-with open("cisco_cli_commands.json", "r") as file:
-    cisco_commands = json.load(file)
-
-# 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 cisco_commands:
-        if entry["command"] in user_input.lower():  # Match the command with user input (case-insensitive)
-            return f"**Command:** {entry['command']}\n\n**Description:** {entry['description']}"
-    return None  # 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": dataset_response})
-        return chat_history
-
-    # If no match, fallback to the LLM
-    outputs = pipeline(user_input, max_new_tokens=256)
-    
-    # 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")
-    user_input = gr.Textbox(placeholder="Enter your Cisco switch/router question here...", label="Your Input")
-    clear_btn = gr.Button("Clear")
-    
-    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])
-
-    clear_btn.click(lambda: [], None, chatbot, queue=False)
-
-# Launch the Gradio app
-iface.launch()