# import os
# import gradio as gr
# from huggingface_hub import InferenceClient
# from dotenv import load_dotenv

# # Load environment variables
# load_dotenv()
# HF_TOKEN = os.getenv("HF_TOKEN")

# # Initialize Hugging Face Inference Client
# client = InferenceClient(
#     model="Qwen/Qwen2.5-Coder-7B-Instruct",
#     token=HF_TOKEN
# )

# # System prompt for coding assistant
# system_message = (
#     "You are a helpful and experienced coding assistant specialized in web development. "
#     "Help the user by generating complete and functional code for building websites. "
#     "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) "
#     "based on their requirements."
# )

# # Streaming chatbot logic
# def respond(message, history):
#     # Prepare messages with system prompt
#     messages = [{"role": "system", "content": system_message}]
#     for user_msg, assistant_msg in history:
#         messages.append({"role": "user", "content": user_msg})
#         messages.append({"role": "assistant", "content": assistant_msg})
#     messages.append({"role": "user", "content": message})

#     # Stream response from the model
#     response = ""
#     for chunk in client.chat.completions.create(
#         model="Qwen/Qwen2.5-Coder-7B-Instruct",
#         messages=messages,
#         max_tokens=2048,
#         temperature=0.7,
#         top_p=0.95,
#         stream=True,
#     ):
#         # Safely handle empty choices
#         if not chunk.choices:
#             continue
            
#         # Safely extract token content
#         token = chunk.choices[0].delta.content or ""
#         response += token
#         yield response

# # Create Gradio interface
# with gr.Blocks() as demo:
#     chatbot = gr.Chatbot(type='messages')  # Use modern message format
#     gr.ChatInterface(fn=respond, chatbot=chatbot, type="messages")  # Match format
# # Launch app
# if __name__ == "__main__":
#     demo.launch()


# import os
# import gradio as gr
# from dotenv import load_dotenv
# from huggingface_hub import InferenceClient

# # Load environment variables
# load_dotenv()

# HF_TOKEN = os.getenv("HF_TOKEN")

# if not HF_TOKEN:
#     raise ValueError("HF_TOKEN is missing.")

# # Initialize client
# client = InferenceClient(
#     api_key=HF_TOKEN
# )

# # System prompt
# system_message = (
#     "You are a helpful and experienced coding assistant specialized in web development. "
#     "Help the user by generating complete and functional code for building websites. "
#     "You can provide HTML, CSS, JavaScript, and backend code like Flask, Node.js, etc. "
#     "based on their requirements."
# )

# def chat_function(message, history):

#     messages = [
#         {
#             "role": "system",
#             "content": system_message
#         }
#     ]

#     history = history or []

#     for item in history:

#         if isinstance(item, dict):

#             role = item.get("role")
#             content = item.get("content", "")

#             if role in ["user", "assistant"]:
#                 messages.append({
#                     "role": role,
#                     "content": content
#                 })

#         elif isinstance(item, (list, tuple)) and len(item) == 2:

#             user_msg, assistant_msg = item

#             if user_msg:
#                 messages.append({
#                     "role": "user",
#                     "content": user_msg
#                 })

#             if assistant_msg:
#                 messages.append({
#                     "role": "assistant",
#                     "content": assistant_msg
#                 })

#     messages.append({
#         "role": "user",
#         "content": message
#     })

#     try:

#         completion = client.chat.completions.create(
#             model="Qwen/Qwen2.5-Coder-7B-Instruct:nscale",
#             messages=messages,
#             max_tokens=2048,
#             temperature=0.7,
#             top_p=0.95,
#         )

#         return completion.choices[0].message.content

#     except Exception as e:
#         return f"Error: {str(e)}"

# # Interface
# demo = gr.ChatInterface(
#     fn=chat_function,
#     type="messages",
#     title="AI Coding Assistant",
#     description="A coding assistant powered by Qwen2.5-Coder."
# )

# # Launch
# if __name__ == "__main__":
#     demo.launch()

import os
import gradio as gr
from dotenv import load_dotenv
from huggingface_hub import InferenceClient

# Load environment variables
load_dotenv()

HF_TOKEN = os.getenv("HF_TOKEN")

if not HF_TOKEN:
    raise ValueError("HF_TOKEN is missing.")

# Initialize client with featherless-ai router
client = InferenceClient(
    base_url="https://router.huggingface.co/featherless-ai/v1",
    api_key=HF_TOKEN,
)

# System prompt
system_message = (
    "You are a helpful and experienced coding assistant specialized in web development. "
    "Help the user by generating complete and functional code for building websites. "
    "You can provide HTML, CSS, JavaScript, and backend code like Flask, Node.js, etc. "
    "based on their requirements."
)

def chat_function(message, history):

    messages = [
        {
            "role": "system",
            "content": system_message
        }
    ]

    history = history or []

    for item in history:

        if isinstance(item, dict):
            role = item.get("role")
            content = item.get("content", "")
            if role in ["user", "assistant"]:
                messages.append({
                    "role": role,
                    "content": content
                })

        elif isinstance(item, (list, tuple)) and len(item) == 2:
            user_msg, assistant_msg = item
            if user_msg:
                messages.append({
                    "role": "user",
                    "content": user_msg
                })
            if assistant_msg:
                messages.append({
                    "role": "assistant",
                    "content": assistant_msg
                })

    messages.append({
        "role": "user",
        "content": message
    })

    try:
        completion = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-7B-Instruct",
            messages=messages,
            max_tokens=2048,
            temperature=0.7,
            top_p=0.95,
        )
        return completion.choices[0].message.content

    except Exception as e:
        return f"Error: {str(e)}"

# Interface
demo = gr.ChatInterface(
    fn=chat_function,
    type="messages",
    title="AI Coding Assistant",
    description="A coding assistant powered by Qwen2.5-Coder."
)

if __name__ == "__main__":
    demo.launch()