app.py

# Welcome to Team Tonic's MultiMed
import os
import numpy as np
import base64
import torch
import torchaudio
import gradio as gr
import requests
import json
import dotenv
from transformers import AutoProcessor, SeamlessM4TModel
import torchaudio
dotenv.load_dotenv()


AUDIO_SAMPLE_RATE = 16000.0
MAX_INPUT_AUDIO_LENGTH = 60  # in seconds
DEFAULT_TARGET_LANGUAGE = "English"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

processor = AutoProcessor.from_pretrained("ylacombe/hf-seamless-m4t-large")
model = SeamlessM4TModel.from_pretrained("ylacombe/hf-seamless-m4t-large").to(device)

from lang_list import (
    LANGUAGE_NAME_TO_CODE,
    S2ST_TARGET_LANGUAGE_NAMES,
    S2TT_TARGET_LANGUAGE_NAMES,
    T2TT_TARGET_LANGUAGE_NAMES,
    TEXT_SOURCE_LANGUAGE_NAMES,
    LANG_TO_SPKR_ID,
)    

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

#processor = AutoProcessor.from_pretrained("ylacombe/hf-seamless-m4t-large")
#model = SeamlessM4TModel.from_pretrained("ylacombe/hf-seamless-m4t-large").to(device)


def process_speech(sound):
    """
    processing sound using seamless_m4t
    """
    # task_name = "T2TT"
    arr, org_sr = torchaudio.load(sound)
    target_language_code = LANGUAGE_NAME_TO_CODE[DEFAULT_TARGET_LANGUAGE]
    new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE)
    max_length = int(MAX_INPUT_AUDIO_LENGTH * AUDIO_SAMPLE_RATE)
    if new_arr.shape[1] > max_length:
        new_arr = new_arr[:, :max_length]
        gr.Warning(f"Input audio is too long. Only the first {MAX_INPUT_AUDIO_LENGTH} seconds is used.")
    input_data = processor(audios = new_arr, sampling_rate=AUDIO_SAMPLE_RATE, return_tensors="pt").to(device)
    tokens_ids = model.generate(**input_data, generate_speech=False, tgt_lang=target_language_code, num_beams=5, do_sample=True)[0].cpu().squeeze().detach().tolist()
    text_out = processor.decode(tokens_ids, skip_special_tokens=True)

    return text_out
    

def convert_image_to_required_format(image):
    """
    convert image from numpy to base64
    """
    if type(image) == type(np.array([])):
        return base64.b64encode(image).decode('utf-8')


def process_image_with_openai(image):
    image_data = convert_image_to_required_format(image)
    openai_api_key = os.getenv('OPENAI_API_KEY') 
    if openai_api_key is None:
        raise Exception("OPENAI_API_KEY not found in environment variables")

    data_payload = {
        "model": "gpt-4-vision-preview",
        "messages": [
            {
                "role": "user",
                "content": image_data
            }
        ],
        "max_tokens": 300
    }

    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers={
            "Content-Type": "application/json",
            "Authorization": f"Bearer {openai_api_key}"
        },
        json=data_payload
    )

    if response.status_code == 200:
        return response.json()['choices'][0]['message']['content']
    else:
        raise Exception(f"OpenAI Error: {response.status_code}")


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    
                for source in response_set.get('response', [])[:5]:  # Limit to top 5 sources.  
                    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:** {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_and_query(text, image,audio):
    try:
        # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
        if image is not None:
            text = process_image_with_openai(image)

        if audio is not None:
            audio = audio[0].numpy()
            audio = audio.astype(np.float32)
            audio = audio / np.max(np.abs(audio))
            audio = audio * 32768
            audio = audio.astype(np.int16)
            audio = audio.tobytes()
            audio = base64.b64encode(audio).decode('utf-8')
        
        # Now, use the text (either provided by the user or obtained from OpenAI) to query Vectara
        vectara_response_json = query_vectara(text)
        markdown_output = convert_to_markdown(vectara_response_json)
        return markdown_output
    except Exception as e:
        return str(e)

# Define the Gradio interface
iface = gr.Interface(
    fn=process_and_query,
    inputs=[
        gr.Textbox(label="Input Text"),
        gr.Image(label="Upload Image"),
        gr.Audio(sources="microphone"),
    ],
    outputs=[gr.Markdown(label="Output Text")],
    title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
    description = '''
            ### 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)"
            ''',
    theme='ParityError/Anime',
    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?"],
    ],
)

iface.launch()