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

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):
    # 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}"


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 education consultant ready to do just about anything for his students. You are discussing training cases with students at TonicUniversity. You will recieve keywords or an image description. 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. You will recieve a summary assessment in a language, always provide a complete answer. 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 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)
            print("Audio Text:", audio_text)  # Debug print
            combined_text += "\n" + audio_text

        # Process image input
        if image_input is not None:
            image_text = process_image(image_input)  # Call process_image with only the image input
            print("Image Text:", image_text)  # Debug print
            combined_text += "\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)
        print("Vectara Response:", vectara_response_json)  # Debug print
        
        # Convert the Vectara response to Markdown
        markdown_output = convert_to_markdown(vectara_response_json)
        
        # Append the original image description to the markdown output
        if image_description:
            markdown_output += "\n\n**Original Image Description:**\n" + image_description
        
        # Process the summary with OpenAI
        final_response = process_summary_with_openai(markdown_output)
        print("Final Response:", final_response)  # Debug print

        # Evaluate hallucination
        hallucination_label = evaluate_hallucination(final_response, markdown_output)
        print("Hallucination Label:", hallucination_label)  # Debug print
        
        return final_response, hallucination_label
    except Exception as e:
        # Handle exceptions
        print(f"An error occurred: {e}")
        return "Error occurred during processing.", "No hallucination evaluation"



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)