app.py

# Welcome to Team Tonic's MultiMed

from gradio_client import Client
import os
import numpy as np
import base64
import gradio as gr
import tempfile
import requests
import json
import dotenv
from scipy.io.wavfile import write
import PIL
from openai import OpenAI
import time
from PIL import Image
import io
import hashlib
import datetime
from utils import build_logger
from transformers import AutoTokenizer, MistralForCausalLM
import torch
import random
from textwrap import wrap
import transformers
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
import torch
import os

# Global variables to hold component references
components = {}
dotenv.load_dotenv()
seamless_client = Client("facebook/seamless_m4t")
HuggingFace_Token = os.getenv("HuggingFace_Token")
hf_token = os.getenv("HuggingFace_Token")
base_model_id = os.getenv('BASE_MODEL_ID', 'default_base_model_id')
model_directory = os.getenv('MODEL_DIRECTORY', 'default_model_directory')
device = "cuda" if torch.cuda.is_available() else "cpu"


def check_hallucination(assertion,citation):
    API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
    headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
    payload = {"inputs" : f"{assertion} [SEP] {citation}"}

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

    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 {HuggingFace_Token}"}

# Function to query the API
def query(payload):
    response = requests.post(VAPI_URL, headers=headers, json=payload)
    return response.json()

# Function to evaluate hallucination
def evaluate_hallucination(input1, input2):
    # Combine the inputs
    combined_input = f"{input1}. {input2}"
    
    # Make the API call
    output = query({"inputs": combined_input})
    
    # Extract the score from the output
    score = output[0][0]['score']
    
    # Generate a label based on the score
    if score < 0.5:
        label = f"🔴 High risk. Score: {score:.2f}"
    else:
        label = f"🟢 Low risk. Score: {score:.2f}"
    
    return label

def process_speech(input_language, audio_input):
    """
    processing sound using seamless_m4t
    """
    if audio_input is None :
        return "no audio or audio did not save yet \nplease try again ! "
    print(f"audio : {audio_input}")
    print(f"audio type : {type(audio_input)}")
    out = seamless_client.predict(
        "S2TT",
        "file",
        None,
        audio_input, #audio_name
        "",
        input_language,# source language
        "English",# target language
        api_name="/run",
    )
    out = out[1] # get the text
    try :
        return f"{out}"
    except Exception as e :
        return f"{e}"

def decode_image(encoded_image: str) -> Image:
    decoded_bytes = base64.b64decode(encoded_image.encode("utf-8"))
    buffer = io.BytesIO(decoded_bytes)
    image = Image.open(buffer)
    return image


def encode_image(image: Image.Image, format: str = "PNG") -> str:
    with io.BytesIO() as buffer:
        image.save(buffer, format=format)
        encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
    return encoded_image


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_conv_image_dir():
    name = os.path.join(LOGDIR, "images")
    os.makedirs(name, exist_ok=True)
    return name


def get_image_name(image, image_dir=None):
    buffer = io.BytesIO()
    image.save(buffer, format="PNG")
    image_bytes = buffer.getvalue()
    md5 = hashlib.md5(image_bytes).hexdigest()

    if image_dir is not None:
        image_name = os.path.join(image_dir, md5 + ".png")
    else:
        image_name = md5 + ".png"

    return image_name

def resize_image(image, max_size):
    width, height = image.size
    aspect_ratio = float(width) / float(height)

    if width > height:
        new_width = max_size
        new_height = int(new_width / aspect_ratio)
    else:
        new_height = max_size
        new_width = int(new_height * aspect_ratio)

    resized_image = image.resize((new_width, new_height))
    return resized_image



def process_image(image_input):
    # Initialize the Gradio client with the URL of the Gradio server
    client = Client("https://adept-fuyu-8b-demo.hf.space/--replicas/pqjvl/")

    # Check if the image input is a NumPy array
    if isinstance(image_input, np.ndarray):
        # Convert the NumPy array to a PIL Image
        image = Image.fromarray(image_input)
        # Save the PIL Image to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file:
            image.save(tmp_file.name)
            image_path = tmp_file.name
    elif isinstance(image_input, str):
        try:
            # Try to decode if it's a base64 string
            image = decode_image(image_input)
        except Exception:
            # If decoding fails, assume it's a file path or a URL
            image_path = image_input
        else:
            # If decoding succeeds, save the decoded image to a temporary file
            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file:
                image.save(tmp_file.name)
                image_path = tmp_file.name
    else:
        # Assuming it's a PIL Image, save it to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_file:
            image_input.save(tmp_file.name)
            image_path = tmp_file.name

    # Call the predict method of the client
    result = client.predict(
        image_path,  # File path or URL of the image
        True,        # Additional parameter for the server (e.g., enable detailed captioning)
        fn_index=2   # Function index if the server has multiple functions
    )

    # Clean up the temporary file if created
    if not isinstance(image_input, str) or isinstance(image_input, str) and 'tmp' in image_path:
        os.remove(image_path)

    return result


def query_vectara(text):
    user_message = text

    # Read authentication parameters from the .env file
    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}"


# Functions to Wrap the Prompt Correctly
def wrap_text(text, width=90):
    lines = text.split('\n')
    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
    wrapped_text = '\n'.join(wrapped_lines)
    return wrapped_text
def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):

    # Combine user input and system prompt
    formatted_input = f"{user_input}{system_prompt}"

    # Encode the input text
    encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
    model_inputs = encodeds.to(device)

    # Generate a response using the model
    output = model.generate(
        **model_inputs,
        max_length=max_length,
        use_cache=True,
        early_stopping=True,
        bos_token_id=model.config.bos_token_id,
        eos_token_id=model.config.eos_token_id,
        pad_token_id=model.config.eos_token_id,
        temperature=0.1,
        do_sample=True
    )

    # Decode the response
    response_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return response_text

# Instantiate the Tokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True, padding_side="left")
# tokenizer = AutoTokenizer.from_pretrained("Tonic/stablemed", trust_remote_code=True, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'left'

# Load the PEFT model
peft_config = PeftConfig.from_pretrained("Tonic/stablemed", token=hf_token)
peft_model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True)
peft_model = PeftModel.from_pretrained(peft_model, "Tonic/stablemed", token=hf_token)


class ChatBot:
    def __init__(self):
        self.history = []

    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
        formatted_input = f"{system_prompt}{user_input}"
        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
        response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
        return response_text

bot = ChatBot()

def process_summary_with_stablemed(summary):
    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

# Main function to handle the Gradio interface logic


def process_and_query(input_language=None, audio_input=None, image_input=None, text_input=None):
    try:
        # Initialize the conditional variables
        combined_text = ""
        image_description = "" 

        # Process text input
        if text_input is not None:
            combined_text = "The user asks the following to his health adviser: " + text_input

        # Process audio input
        if audio_input is not None:
            audio_text = process_speech(input_language, audio_input)
            combined_text += "\n\n**Audio Input:**\n" + audio_text

        # Process image input
        if image_input is not None:
            image_text = process_image(image_input)
            combined_text += "\n\n**Image Input:**\n" + image_text

        # Check if combined text is empty
        if not combined_text.strip():
            return "Error: Please provide some input (text, audio, or image).", "No hallucination evaluation"
            
        # Use the text to query Vectara
        vectara_response_json = query_vectara(combined_text)

        # Parse the Vectara response
        vectara_response = json.loads(vectara_response_json)
        summary = vectara_response.get('summary', 'No summary available')
        sources_info = vectara_response.get('sources', [])

        # Format Vectara response in Markdown
        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"

        # Append the original image description in Markdown
        if image_description:
            markdown_output += "\n### Original Image Description\n"
            markdown_output += image_description + "\n"

        # Process the summary with OpenAI
        final_response = process_summary_with_stablemed(summary)

        # Evaluate hallucination
        hallucination_label = evaluate_hallucination(final_response, summary)

        # Add final response and hallucination label to Markdown output
        markdown_output += "\n### Processed Summary with StableMed\n"
        markdown_output += final_response + "\n"
        markdown_output += "\n### Hallucination Evaluation\n"
        markdown_output += f"* **Label**: {hallucination_label}\n"

        return markdown_output, hallucination_label
    except Exception as e:
        return f"Error occurred during processing: {e}", "No hallucination evaluation"



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

🗣️📝 This is an educational and accessible conversational tool.

### How To Use ⚕🗣️😷MultiMed⚕: 

🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using image, audio or text!

📚🌟💼 that uses [Tonic/stablemed](https://huggingface.co/Tonic/stablemed) and [adept/fuyu-8B](https://huggingface.co/adept/fuyu-8b) with [Vectara](https://huggingface.co/vectara) embeddings + retrieval. 
do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷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 = [
    "Afrikaans",
    "Amharic",
    "Modern Standard Arabic",
    "Moroccan Arabic",
    "Egyptian Arabic",
    "Assamese",
    "Asturian",
    "North Azerbaijani",
    "Belarusian",
    "Bengali",
    "Bosnian",
    "Bulgarian",
    "Catalan",
    "Cebuano",
    "Czech",
    "Central Kurdish",
    "Mandarin Chinese",
    "Welsh",
    "Danish",
    "German",
    "Greek",
    "English",
    "Estonian",
    "Basque",
    "Finnish",
    "French",
    "West Central Oromo",
    "Irish",
    "Galician",
    "Gujarati",
    "Hebrew",
    "Hindi",
    "Croatian",
    "Hungarian",
    "Armenian",
    "Igbo",
    "Indonesian",
    "Icelandic",
    "Italian",
    "Javanese",
    "Japanese",
    "Kamba",
    "Kannada",
    "Georgian",
    "Kazakh",
    "Kabuverdianu",
    "Halh Mongolian",
    "Khmer",
    "Kyrgyz",
    "Korean",
    "Lao",
    "Lithuanian",
    "Luxembourgish",
    "Ganda",
    "Luo",
    "Standard Latvian",
    "Maithili",
    "Malayalam",
    "Marathi",
    "Macedonian",
    "Maltese",
    "Meitei",
    "Burmese",
    "Dutch",
    "Norwegian Nynorsk",
    "Norwegian Bokmål",
    "Nepali",
    "Nyanja",
    "Occitan",
    "Odia",
    "Punjabi",
    "Southern Pashto",
    "Western Persian",
    "Polish",
    "Portuguese",
    "Romanian",
    "Russian",
    "Slovak",
    "Slovenian",
    "Shona",
    "Sindhi",
    "Somali",
    "Spanish",
    "Serbian",
    "Swedish",
    "Swahili",
    "Tamil",
    "Telugu",
    "Tajik",
    "Tagalog",
    "Thai",
    "Turkish",
    "Ukrainian",
    "Urdu",
    "Northern Uzbek",
    "Vietnamese",
    "Xhosa",
    "Yoruba",
    "Cantonese",
    "Colloquial Malay",
    "Standard Malay",
    "Zulu"
]

def clear():
    # Return default values
    return "None", None, None, "", [], [], []


def create_interface():
    with gr.Blocks(theme='ParityError/Anime') as iface:
        # Add a 'None' or similar option to represent no selection
        input_language_options = ["None"] + languages
        input_language = gr.Dropdown(input_language_options, label="Select the language", value="None", interactive=True)

        with gr.Accordion("Use Voice", open=False) as voice_accordion:
            audio_input = gr.Audio(label="Speak", type="filepath", sources="microphone")
            audio_output = gr.Markdown(label="Output text")  # Markdown component for audio

        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

        with gr.Accordion("MultiMed", open=False) as multimend_accordion:
            text_input = gr.Textbox(label="Use Text", lines=3)
            text_output = gr.Markdown(label="Output text")  # Markdown component for text

        text_button = gr.Button("Use MultiMed")
        hallucination_output = gr.Label(label="Hallucination Evaluation")
        text_button.click(process_and_query, inputs=[input_language, audio_input, image_input, text_input], outputs=[text_output, hallucination_output])

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

    return iface

iface = create_interface()
iface.launch(show_error=True, debug=True, share=True)