from gradio_client import Client
import numpy as np
import gradio as gr
import requests
import json
import dotenv
import soundfile as sf
import time
import textwrap
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
import uuid
import optimum


welcome_message = """
# 👋🏻Welcome to ⚕🗣️😷TruEra - MultiMed ⚕🗣️😷

🗣️📝 This is an accessible and multimodal tool optimized using TruEra! We evaluated several configurations, prompts, and models to optimize this application. 

### How To Use ⚕🗣️😷TruEra - MultiMed⚕: 

🗣️📝Interact with ⚕🗣️😷TruEra - MultiMed⚕ in any language using image, audio or text. ⚕🗣️😷TruEra - MultiMed is an accessible application 📚🌟💼 that uses [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) and [Tonic1/Official-Qwen-VL-Chat](https://huggingface.co/Qwen/Qwen-VL-Chat) with [Vectara](https://huggingface.co/vectara) embeddings + retrieval w/ [facebook/seamless-m4t-v2-large](https://huggingface.co/facebook/hf-seamless-m4t-large) for audio translation & accessibility.
do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷TruEra MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/TeamTonic/MultiMed?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3>
### Join us : 

🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"             
"""


languages = {
    "English": "eng",
    "Modern Standard Arabic": "arb",
    "Bengali": "ben",
    "Catalan": "cat",
    "Czech": "ces",
    "Mandarin Chinese": "cmn",
    "Welsh": "cym",
    "Danish": "dan",
    "German": "deu",
    "Estonian": "est",
    "Finnish": "fin",
    "French": "fra",
    "Hindi": "hin",
    "Indonesian": "ind",
    "Italian": "ita",
    "Japanese": "jpn",
    "Korean": "kor",
    "Maltese": "mlt",
    "Dutch": "nld",
    "Western Persian": "pes",
    "Polish": "pol",
    "Portuguese": "por",
    "Romanian": "ron",
    "Russian": "rus",
    "Slovak": "slk",
    "Spanish": "spa",
    "Swedish": "swe",
    "Swahili": "swh",
    "Telugu": "tel",
    "Tagalog": "tgl",
    "Thai": "tha",
    "Turkish": "tur",
    "Ukrainian": "ukr",
    "Urdu": "urd",
    "Northern Uzbek": "uzn",
    "Vietnamese": "vie"
}


# Global variables to hold component references
components = {}
dotenv.load_dotenv()
seamless_client = Client("https://facebook-seamless-m4t.hf.space/--replicas/7sv2b/") #TruEra
HuggingFace_Token = os.getenv("HuggingFace_Token")
hf_token = os.getenv("HuggingFace_Token")
device = "cuda" if torch.cuda.is_available() else "cpu"

image_description = "" 
# audio_output = ""
# global markdown_output
# global audio_output


def check_hallucination(assertion, citation):
    print("Entering check_hallucination function")
    api_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
    header = {"Authorization": f"Bearer {hf_token}"}
    payload = {"inputs": f"{assertion} [SEP] {citation}"}

    response = requests.post(api_url, headers=header, json=payload, timeout=120)
    output = response.json()
    output = output[0][0]["score"]

    print(f"check_hallucination output: {output}")
    return f"**hallucination score:** {output}"


# Define the API parameters
vapi_url = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"

headers = {"Authorization": f"Bearer {hf_token}"}


# Function to query the API
def query(payload):
    print("Entering query function")
    response = requests.post(vapi_url, headers=headers, json=payload)
    print(f"API response: {response.json()}")
    return response.json()

# Function to evaluate hallucination
def evaluate_hallucination(input1, input2):
    print("Entering evaluate_hallucination function")
    combined_input = f"{input1}[SEP]{input2}"
    
    output = query({"inputs": combined_input})
    score = output[0][0]['score']

    if score < 0.5:
        label = f"🔴 High risk. Score: {score:.2f}"
    else:
        label = f"🟢 Low risk. Score: {score:.2f}"

    print(f"evaluate_hallucination label: {label}")
    return label

def save_audio(audio_input, output_dir="saved_audio"):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Extract sample rate and audio data
    sample_rate, audio_data = audio_input

    # Generate a unique file name
    file_name = f"audio_{int(time.time())}.wav"
    file_path = os.path.join(output_dir, file_name)

    # Save the audio file
    sf.write(file_path, audio_data, sample_rate)

    return file_path


def save_image(image_input, output_dir="saved_images"):
    print("Entering save_image function")
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    if isinstance(image_input, np.ndarray):
        image = Image.fromarray(image_input)
        file_name = f"image_{int(time.time())}.png"
        file_path = os.path.join(output_dir, file_name)
        image.save(file_path)

        print(f"Image saved at: {file_path}")
        return file_path
    else:
        raise ValueError("Invalid image input type")


def process_image(image_file_path):
    print("Entering process_image function")
    client = Client("https://tonic1-official-qwen-vl-chat.hf.space/--replicas/86976/")  # TruEra
    try:
        result = client.predict(
            "Describe this image in detail, identify every detail in this image. Describe the image the best you can.",
            image_file_path,
            fn_index=0
        )
        print(f"Image processing result: {result}")
        return result
    except Exception as e:
        print(f"Error in process_image: {e}")
        return f"Error occurred during image processing: {e}"


def process_speech(audio_input, source_language, target_language="English"):
    print("Entering process_speech function")
    if audio_input is None:
        return "No audio input provided."
    try:
        result = seamless_client.predict(
            audio_input,
            source_language,
            target_language,
            api_name="/s2tt"
        )
        print(f"Speech processing result: {result}")
        return result
    except Exception as e:
        print(f"Error in process_speech: {str(e)}")
        return f"Error in speech processing: {str(e)}"

def convert_text_to_speech(input_text, source_language, target_language):
    print("Entering convert_text_to_speech function")
    try:
        result = seamless_client.predict(
            input_text,  
            source_language, 
            target_language,  
            api_name="/t2st"
        )
        audio_file_path = result[0] if result else None
        translated_text = result[1] if result else ""

        print(f"Text-to-speech conversion result: Audio file path: {audio_file_path}, Translated text: {translated_text}")
        return audio_file_path, translated_text
    except Exception as e:
        print(f"Error in convert_text_to_speech: {str(e)}")
        return None, f"Error in text-to-speech conversion: {str(e)}"

def query_vectara(text):
    user_message = text
    customer_id = os.getenv('CUSTOMER_ID')
    corpus_id = os.getenv('CORPUS_ID')
    api_key = os.getenv('API_KEY')

    # Define the headers
    api_key_header = {
        "customer-id": customer_id,
        "x-api-key": api_key
    }

    # Define the request body in the structure provided in the example
    request_body = {
        "query": [
            {
                "query": user_message,
                "queryContext": "",
                "start": 1,
                "numResults": 25,
                "contextConfig": {
                    "charsBefore": 0,
                    "charsAfter": 0,
                    "sentencesBefore": 2,
                    "sentencesAfter": 2,
                    "startTag": "%START_SNIPPET%",
                    "endTag": "%END_SNIPPET%",
                },
                "rerankingConfig": {
                    "rerankerId": 272725718,
                    "mmrConfig": {
                        "diversityBias": 0.35
                    }
                },
                "corpusKey": [
                    {
                        "customerId": customer_id,
                        "corpusId": corpus_id,
                        "semantics": 0,
                        "metadataFilter": "",
                        "lexicalInterpolationConfig": {
                            "lambda": 0
                        },
                        "dim": []
                    }
                ],
                "summary": [
                    {
                        "maxSummarizedResults": 5,
                        "responseLang": "auto",
                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
                    }
                ]
            }
        ]
    }

    # Make the API request using Gradio
    response = requests.post(
        "https://api.vectara.io/v1/query",
        json=request_body,  # Use json to automatically serialize the request body
        verify=True,
        headers=api_key_header
    )

    if response.status_code == 200:
        query_data = response.json()
        if query_data:
            sources_info = []

            # Extract the summary.
            summary = query_data['responseSet'][0]['summary'][0]['text']

            # Iterate over all response sets
            for response_set in query_data.get('responseSet', []):
                # Extract sources
                # Limit to top 5 sources.
                for source in response_set.get('response', [])[:5]:
                    source_metadata = source.get('metadata', [])
                    source_info = {}

                    for metadata in source_metadata:
                        metadata_name = metadata.get('name', '')
                        metadata_value = metadata.get('value', '')

                        if metadata_name == 'title':
                            source_info['title'] = metadata_value
                        elif metadata_name == 'author':
                            source_info['author'] = metadata_value
                        elif metadata_name == 'pageNumber':
                            source_info['page number'] = metadata_value

                    if source_info:
                        sources_info.append(source_info)

            result = {"summary": summary, "sources": sources_info}
            return f"{json.dumps(result, indent=2)}"
        else:
            return "No data found in the response."
    else:
        return f"Error: {response.status_code}"


def wrap_text(text, width=90):
    print("Wrapping text...")
    lines = text.split('\n')
    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
    wrapped_text = '\n'.join(wrapped_lines)
    return wrapped_text

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-1_8B-Chat", trust_remote_code=True) #TruEra
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-1_8B-Chat", device_map="auto", trust_remote_code=True).eval()

class ChatBot:
    def __init__(self):
        self.history = None

    def predict(self, user_input, system_prompt=""):
        print("Generating prediction...")
        response, self.history = model.chat(tokenizer, user_input, history=self.history, system=system_prompt)
        return response

bot = ChatBot()

def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):
    print("Processing multimodal prompt...")
    return bot.predict(user_input, system_prompt)

def process_summary_with_qwen(summary):
    print("Processing summary with Qwen...")
    system_prompt = "You are a medical instructor. Assess and describe the proper options to your students in minute detail. Propose a course of action for them to base their recommendations on based on your description."
    response_text = bot.predict(summary, system_prompt)
    return response_text



def process_and_query(input_language=None, audio_input=None, image_input=None, text_input=None):
    try:
        print("Processing and querying...")
        combined_text = ""
        markdown_output = ""
        image_text = ""
        print(f"Image Input Type: {type(image_input)}, Audio Input Type: {type(audio_input)}")

        if image_input is not None:
            print("Processing image input...")
            image_file_path = save_image(image_input)
            image_text = process_image(image_file_path)
            combined_text += "\n\n**Image Input:**\n" + image_text

        elif audio_input is not None:
            print("Processing audio input...")
            sample_rate, audio_data = audio_input
            audio_file_path = save_audio(audio_input)
            audio_text = process_speech(audio_file_path, input_language, "English")
            combined_text += "\n\n**Audio Input:**\n" + audio_text

        elif text_input is not None and text_input.strip():
            print("Processing text input...")
            combined_text += "The user asks the query above to his health adviser: " + text_input

        else:
            return "Error: Please provide some input (text, audio, or image)."

        if image_text:
            markdown_output += "\n### Original Image Description\n"
            markdown_output += image_text + "\n"

        print("Querying Vectara...")
        vectara_response_json = query_vectara(combined_text)
        vectara_response = json.loads(vectara_response_json)
        summary = vectara_response.get('summary', 'No summary available')
        sources_info = vectara_response.get('sources', [])

        markdown_output = "### Vectara Response Summary\n"
        markdown_output += f"* **Summary**: {summary}\n"
        markdown_output += "### Sources Information\n"
        for source in sources_info:
            markdown_output += f"* {source}\n"

        final_response = process_summary_with_qwen(summary)

        print("Converting text to speech...")
        target_language = "English"
        audio_output, translated_text = convert_text_to_speech(final_response, target_language, input_language)

        print("Evaluating hallucination...")
        try:
            hallucination_label = evaluate_hallucination(final_response, summary)
        except Exception as e:
            print(f"Error in hallucination evaluation: {e}")
            hallucination_label = "Evaluation skipped due to the model loading. For evaluation results, please try again in 29 minutes."


        markdown_output += "\n### Processed Summary with Qwen\n"
        markdown_output += final_response + "\n"
        markdown_output += "\n### Hallucination Evaluation\n"
        markdown_output += f"* **Label**: {hallucination_label}\n"
        markdown_output += "\n### Translated Text\n"
        markdown_output += translated_text + "\n"

        return markdown_output, audio_output

    except Exception as e:
        print(f"Error occurred: {e}")
        return f"Error occurred during processing: {e}.", None


def clear():
    return "English", None, None, "", None


def create_interface():
    with gr.Blocks(theme='ParityError/Anime') as interface:
        # Display the welcome message
        gr.Markdown(welcome_message)
        # Extract the full names of the languages
        language_names = list(languages.keys())

        # Add a 'None' or similar option to represent no selection
        input_language_options = ["None"] + language_names

        # Create a dropdown for language selection
        input_language = gr.Dropdown(input_language_options, label="Select the language", value="English", interactive=True)
        
        with gr.Accordion("Use Voice", open=False) as voice_accordion:
            audio_input = gr.Audio(label="Speak")
            audio_output = gr.Markdown(label="Output text")  # Markdown component for audio
            gr.Examples([["audio1.wav"], ["audio2.wav"], ], inputs=[audio_input])

        with gr.Accordion("Use a Picture", open=False) as picture_accordion:
            image_input = gr.Image(label="Upload image")
            image_output = gr.Markdown(label="Output text")  # Markdown component for image
            gr.Examples([["image1.png"], ["image2.jpeg"], ["image3.jpeg"], ], inputs=[image_input])

        with gr.Accordion("MultiMed", open=False) as multimend_accordion:
            text_input = gr.Textbox(label="Use Text", lines=3, placeholder="I have had a sore throat and phlegm for a few days and now my cough has gotten worse!")
        
            gr.Examples([
                ["What is the proper treatment for buccal herpes?"],
                ["I have had a sore throat and hoarse voice for several days and now a strong cough recently "],
                ["How does cellular metabolism work TCA cycle"],
                ["What special care must be provided to children with chicken pox?"],
                ["When and how often should I wash my hands?"],
                ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
                ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
                ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
            ], inputs=[text_input])

        text_output = gr.Markdown(label="MultiMed")  
        audio_output = gr.Audio(label="Audio Out", type="filepath")
        
        text_button = gr.Button("Use MultiMed")
        text_button.click(process_and_query, inputs=[input_language, audio_input, image_input, text_input], outputs=[text_output, audio_output])

        clear_button = gr.Button("Clear")
        clear_button.click(clear, inputs=[], outputs=[input_language, audio_input, image_input, text_output, audio_output])

    return interface


app = create_interface()
app.launch(show_error=True, debug=True)