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 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

dotenv.load_dotenv()

seamless_client = Client("facebook/seamless_m4t")
HuggingFace_Token = os.getenv("HuggingFace_Token")

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"**hullicination 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, text_input):
    # Resize the image if needed
    max_image_size = 1024  # You can adjust this size
    image = resize_image(image_input, max_image_size)

    # Encode the image to base64
    base64_image_str = encode_image(image)

    # Prepare the payload for the HTTP request
    payload = {
        "content": [
            {
                "prompt": text_input,
                "image": base64_image_str,
            }
        ],
        "token": "sk-OtterHD",  # Replace with your actual token
    }

    # Specify the URL for the HTTP request
    url = "https://ensures-picture-choices-labels.trycloudflare.com/app/otter"
    headers = {"Content-Type": "application/json"}

    # Make the HTTP request
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    if response.status_code == 200:
        results = response.json()
        return results["result"]
    else:
        return f"Error: {response.status_code}, {response.text}"


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": 50,
                "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 convert_to_markdown(vectara_response_json):
    vectara_response = json.loads(vectara_response_json)
    if vectara_response:
        summary = vectara_response.get('summary', 'No summary available')
        sources_info = vectara_response.get('sources', [])

        # Format the summary as Markdown
        markdown_summary = f' {summary}\n\n'

        # Format the sources as a numbered list
        markdown_sources = ""
        for i, source_info in enumerate(sources_info):
            author = source_info.get('author', 'Unknown author')
            title = source_info.get('title', 'Unknown title')
            page_number = source_info.get('page number', 'Unknown page number')
            markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"

        return f"{markdown_summary}**Sources:**\n{markdown_sources}"
    else:
        return "No data found in the response."
# Main function to handle the Gradio interface logic

def process_summary_with_openai(summary):
    """
    This function takes a summary text as input and processes it with OpenAI's GPT model.
    """
    try:
        # Ensure that the OpenAI client is properly initialized
        client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
        
        # Create the prompt for OpenAI's completion
        prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in English. Exclude any other commentary:"
        
        # Call the OpenAI API with the prompt and the summary
        completion = client.chat.completions.create(
            model="gpt-4-1106-preview",  # Make sure to use the correct model name
            messages=[
                {"role": "system", "content": prompt},
                {"role": "user", "content": summary}
            ]
        )
        
        # Extract the content from the completion
        final_summary = completion.choices[0].message.content
        return final_summary
    except Exception as e:
        return str(e)
        
def process_and_query(input_language=None, audio_input=None, image_input=None, text_input=None):
    try:
        # Initialize the combined text
        combined_text = ""

        # 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" + audio_text

        # Check if only an image is provided without text
        if image_input is not None and not combined_text.strip():
            return "Error: Please provide text input along with the image.", "No hallucination evaluation"

        # Process image input
        if image_input is not None:
            # Use the current combined text (which includes the processed text input) for image processing
            image_text = process_image(image_input, combined_text)
            combined_text += "\n" + image_text
            
        # Use the text to query Vectara
        vectara_response_json = query_vectara(combined_text)
        
        # Convert the Vectara response to Markdown
        markdown_output = convert_to_markdown(vectara_response_json)
        
        # Process the summary with OpenAI
        final_response = process_summary_with_openai(markdown_output)

        # Evaluate hallucination
        hallucination_label = evaluate_hallucination(final_response, markdown_output)
        
        return final_response, hallucination_label

    except Exception as e:
        return str(e), "Error in processing"


welcome_message = """
# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
### How To Use ⚕🗣️😷MultiMed⚕: 
#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health. 
#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, 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"
]


with gr.Blocks(theme='ParityError/Anime') as iface : 
    gr.Markdown(welcome_message)
    with gr.Accordion("speech to text",open=True):
        input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
        audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
        audio_output = gr.Markdown(label="output text")
        # audio_button = gr.Button("process audio")
        # audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
        gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
    with gr.Accordion("image identification",open=True):
        image_input = gr.Image(label="upload image")
        image_output = gr.Markdown(label="output text")
        # image_button = gr.Button("process image")
        # image_button.click(process_image, inputs=image_input, outputs=image_output)
        gr.Examples(["sick person.jpeg"],inputs=[image_input])
    with gr.Accordion("text summarization",open=True):
        text_input = gr.Textbox(label="input text",lines=5)
        text_output = gr.Markdown(label="output text")
        text_button = gr.Button("process text")
        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])
        gr.Examples([
            ["What is the proper treatment for buccal herpes?"],
            ["Male, 40 presenting with swollen glands and a rash"],
            ["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])
    # with gr.Accordion("hallucination check",open=True):
    #     assertion = gr.Textbox(label="assertion")
    #     citation =  gr.Textbox(label="citation text")
    #     hullucination_output = gr.Markdown(label="output text")
    #     hallucination_button = gr.Button("check hallucination")
    #     gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
    #     hallucination_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
    



iface.queue().launch(show_error=True,debug=True)