import pandas as pd
import requests
import os

import gradio

# work around due to HF Spaces bug
if gradio.__version__ != '4.16.0':
    os.system("pip uninstall -y gradio")
    os.system("pip install gradio==4.16.0")

import gradio as gr

from info.train_a_model import (
    LLM_BENCHMARKS_TEXT)
from info.submit import (
    SUBMIT_TEXT)
from info.deployment import (
    DEPLOY_TEXT)
from info.programs import (
    PROGRAMS_TEXT)
from info.citation import(
    CITATION_TEXT)
from info.validated_chat_models import(
    VALIDATED_CHAT_MODELS)
from info.about import(
    ABOUT)
from src.processing import filter_benchmarks_table

inference_endpoint_url = os.environ['inference_endpoint_url']
submission_form_endpoint_url = os.environ['submission_form_endpoint_url']
inference_concurrency_limit = os.environ['inference_concurrency_limit']

demo = gr.Blocks()

with demo:
    
    gr.HTML("""<h1 align="center" id="space-title">🤗Powered-by-Intel LLM Leaderboard 💻</h1>""")
    gr.Markdown("""This leaderboard is designed to evaluate, score, and rank open-source LLMs
                that have been pre-trained or fine-tuned on Intel Hardware 🦾. To submit your model for evaluation,
        follow the instructions and complete the form in the 🏎️ Submit tab. Models submitted to the leaderboard are evaluated 
        on the Intel Developer Cloud ☁️. The evaluation platform consists of Gaudi Accelerators and Xeon CPUs running benchmarks from
        the  [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness).""")
    gr.Markdown("""Join  5000+ developers on the [Intel DevHub Discord](https://discord.gg/yNYNxK2k) to get support with your submission and 
                talk about everything from GenAI, HPC, to Quantum Computing.""")
    gr.Markdown("""A special shout-out to the 🤗 [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) 
                team for generously sharing their code and best 
                practices, ensuring that AI Developers have a valuable and enjoyable tool at their disposal.""")

    def submit_to_endpoint(model_name, revision_name, model_type, hw_type, terms, precision, weight_type, training_infra, affiliation, base_model):
        # Construct the data payload to send
        data = {
            "model_name": model_name,
            "revision_name": revision_name,
            "model_type": model_type,
            "hw_type": hw_type,
            "terms": terms,
            "precision": precision,
            "weight_type": weight_type,
            "training_infrastructure": training_infra,
            "affiliation": affiliation,
            "base_model": base_model
        }
        
        # URL of the endpoint expecting the HTTP request
        url = submission_form_endpoint_url
        
        try:
            response = requests.post(url, json=data)
            if response.status_code == 200:
                return "Submission successful!"
            else:
                return f"Submission failed with status code {response.status_code}"
        except Exception as e:
            return f"Failed to submit due to an error: {str(e)}"
    
    with gr.Accordion("Chat with Top Models on the Leaderboard Here 💬", open=False):
        
        chat_model_dropdown = gr.Dropdown(
                        choices=VALIDATED_CHAT_MODELS,
                        label="Select a leaderboard model to chat with. ",
                        multiselect=False,
                        value=VALIDATED_CHAT_MODELS[0],
                        interactive=True,
                    )
        
        #chat_model_selection = chat_model_dropdown.value
        chat_model_selection = 'Intel/neural-chat-7b-v1-1'
        
        def call_api_and_stream_response(query, chat_model):
            """
            Call the API endpoint and yield characters as they are received.
            This function simulates streaming by yielding characters one by one.
            """
            url = inference_endpoint_url
            params = {"query": query, "selected_model": chat_model}
            with requests.get(url, json=params, stream=True) as r:  # Use params for query parameters
                for chunk in r.iter_content(chunk_size=1):
                    if chunk:
                        yield chunk.decode()

        def get_response(query, history):
            """
            Wrapper function to call the streaming API and compile the response.
            """
            response = ''
            for char in call_api_and_stream_response(query, chat_model=chat_model_selection):
                if char == '<':  # This is stopping condition; adjust as needed.
                    break
                response += char
                yield [(f"🤖 Response from LLM: {chat_model_selection}", response)]  # Correct format for Gradio Chatbot
#

        chatbot = gr.Chatbot()
        msg = gr.Textbox()
        submit = gr.Button("Submit")
        clear = gr.Button("Clear")
        def user(user_message, history):
            return "", history + [[user_message, None]]
        def clear_chat(*args):
            return []  # Returning an empty list to signify clearing the chat, adjust as per Gradio's capabilities
        submit.click(
            fn=get_response,
            inputs=[msg, chatbot],
            outputs=chatbot
        )
        clear.click(
            fn=clear_chat,
            inputs=None,
            outputs=chatbot
        )
        

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏆 LLM Leadeboard", elem_id="llm-benchmark-table", id=0):
            with gr.Row():
                with gr.Column():
                    filter_hw = gr.CheckboxGroup(choices=["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"],
                                     label="Select Training Platform*",
                                     elem_id="compute_platforms",
                                     value=["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"])
                    filter_platform = gr.CheckboxGroup(choices=["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"],
                                     label="Training Infrastructure*",
                                     elem_id="training_infra",
                                     value=["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"])
                    filter_affiliation = gr.CheckboxGroup(choices=["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"],
                                     label="Intel Program Affiliation",
                                     elem_id="program_affiliation",
                                     value=["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"])
                    
                with gr.Column():
                    filter_size = gr.CheckboxGroup(choices=[1,3,5,7,13,35,60,70,100],
                                     label="Model Sizes (Billion of Parameters)",
                                     elem_id="parameter_size",
                                     value=[1,3,5,7,13,35,60,70,100])
                    filter_precision = gr.CheckboxGroup(choices=["fp32","fp16","bf16","int8","fp8", "int4"],
                                     label="Model Precision",
                                     elem_id="precision",
                                     value=["fp32","fp16","bf16","int8","fp8", "int4"])
                    filter_type = gr.CheckboxGroup(choices=["pretrained","fine-tuned","chat-models","merges/moerges"],
                                     label="Model Types",
                                     elem_id="model_types",
                                     value=["pretrained","fine-tuned","chat-models","merges/moerges"])
                    
            initial_df = pd.read_csv("./status/leaderboard_status_030824.csv")
            
            def update_df(hw_selected, platform_selected, affiliation_selected, size_selected, precision_selected, type_selected):
                filtered_df = filter_benchmarks_table(df=initial_df, hw_selected=hw_selected, platform_selected=platform_selected, 
                                                      affiliation_selected=affiliation_selected, size_selected=size_selected, 
                                                      precision_selected=precision_selected, type_selected=type_selected)
                return filtered_df
            
            initial_filtered_df = update_df(["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"], 
                                ["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"], 
                                ["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"], 
                                [1,3,5,7,13,35,60,70,100], 
                                ["fp32","fp16","bf16","int8","fp8", "int4"], 
                                ["pretrained","fine-tuned","chat-models","merges/moerges"])
            
            gradio_df_display = gr.Dataframe(value=initial_filtered_df)
            
            filter_hw.change(fn=update_df, 
                             inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                             outputs=[gradio_df_display])
            filter_platform.change(fn=update_df, 
                                inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                                outputs=[gradio_df_display])
            filter_affiliation.change(fn=update_df, 
                                inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                                outputs=[gradio_df_display])
            filter_size.change(fn=update_df, 
                               inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                               outputs=[gradio_df_display])
            filter_precision.change(fn=update_df, 
                                inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                                outputs=[gradio_df_display])
            filter_type.change(fn=update_df, 
                               inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type], 
                               outputs=[gradio_df_display])
        
            
        with gr.TabItem("🧰 Train a Model", elem_id="getting-started", id=1):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
        with gr.TabItem("🚀 Deployment Tips", elem_id="deployment-tips", id=2):
            gr.Markdown(DEPLOY_TEXT, elem_classes="markdown-text")
        with gr.TabItem("👩‍💻 Developer Programs", elem_id="hardward-program", id=3):
            gr.Markdown(PROGRAMS_TEXT, elem_classes="markdown-text")
        with gr.TabItem("❓ About ", elem_id="about", id=5):
            gr.Markdown(ABOUT, elem_classes="markdown-text")
        with gr.TabItem("🏎️ Submit", elem_id="submit", id=4):
            gr.Markdown(SUBMIT_TEXT, elem_classes="markdown-text")
            with gr.Row():
                gr.Markdown("# Submit Model for Evaluation 🏎️", elem_classes="markdown-text")
            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name", 
                                                    info = """ Name of Model in the Hub. For example: 'Intel/neural-chat-7b-v1-1'""",)
                    revision_name_textbox = gr.Textbox(label="Revision commit (Branch)", placeholder="main")
                    model_type = gr.Dropdown(
                        choices=["pretrained","fine-tuned","chat models","merges/moerges"],
                        label="Model type",
                        multiselect=False,
                        value="pretrained",
                        interactive=True,
                    )
                    
                    hw_type = gr.Dropdown(
                        choices=["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"],
                        label="Training Hardware",
                        multiselect=False,
                        value="Gaudi",
                        interactive=True,
                    )
                    terms = gr.Checkbox(
                        label="Check if you have read and agreed to terms and conditions associated with submitting\
                            a model to the leaderboard.",
                        value=False,
                        interactive=True,
                    )
                    submit_button = gr.Button("🤗 Submit Eval 💻")
                    submission_result = gr.Markdown()
                    )
                with gr.Column():
                    precision = gr.Dropdown(
                        choices=["fp32","fp16","bf16","int8","fp8", "int4"],
                        label="Precision",
                        multiselect=False,
                        value="fp16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=["Original", "Adapter", "Delta"],
                        label="Weights type",
                        multiselect=False,
                        value="Original",
                        interactive=True,
                        info = """ Select the appropriate weights. If you have fine-tuned or adapted a model with PEFT or Delta-Tuning you likely have
                        LoRA Adapters or Delta Weights.""",
                    )
                    training_infra = gr.Dropdown(
                        choices=["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"],
                        label="Training Infrastructure",
                        multiselect=False,
                        value="Intel Developer Cloud",
                        interactive=True,
                        info = """ Select the infrastructure that the model was developed on. 
                        Local is the ideal choice for Core Ultra, ARC GPUs, and local data center infrastructure.""",
                    )
                    affiliation = gr.Dropdown(
                        choices=["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"],
                        label="Affiliation with Intel",
                        multiselect=False,
                        value="No Affiliation",
                        interactive=True,
                        info = """ Select "No Affiliation" if not part of any Intel programs.""",
                    )
                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")

                    submit_button.click(
                        fn=submit_to_endpoint,
                        inputs=[model_name_textbox, revision_name_textbox, model_type, hw_type, terms, precision, weight_type, training_infra, affiliation, base_model_name_textbox],
                        outputs=submission_result
                
           
            
    with gr.Accordion("📙 Citation", open=False):
            citation =gr.Textbox(value = CITATION_TEXT,
                                 lines=6,
                                 label="Use the following to cite this content")
            
    gr.Markdown("""<div style="display: flex; justify-content: center;"> <p> Intel, the Intel logo and Gaudi are trademarks of Intel Corporation or its subsidiaries.
*Other names and brands may be claimed as the property of others.
</p> </div>""")
demo.queue()
demo.launch(share=False)