# 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
dotenv.load_dotenv()
import time
seamless_client = Client("facebook/seamless_m4t")
def process_speech(audio):
"""
processing sound using seamless_m4t
"""
# audio_name = f"{np.random.randint(0, 100)}.wav"
# sr, data = audio
# write(audio_name, sr, data.astype(np.int16))
job = seamless_client.submit(
"S2TT",
"file",
None,
audio, #audio_name
"",
"French",# source language
"English",# target language
api_name="/run",
)
with not job.done() :
time.sleep(2)
out = job.get_result()
out = out[1] # get the text
try :
return f"{out}"
except Exception as e :
return f"{e}"
def process_image(image) :
img_name = f"{np.random.randint(0, 100)}.jpg"
PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
image = open(img_name, "rb").read()
base64_image = base64_image = base64.b64encode(image).decode('utf-8')
openai_api_key = os.getenv('OPENAI_API_KEY')
# oai_org = os.getenv('OAI_ORG')
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {openai_api_key}"
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What's in this image?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
try :
out = response.json()
out = out["choices"][0]["message"]["content"]
return out
except Exception as e :
return f"{e}"
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:** {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=None):
try:
# augment the prompt before feeding it to vectara
text = "the user asks the following to his health adviser " + text
# 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(image)
# return "**Summary:** "+text
# if audio is not None:
# text = process_speech(audio)
# # augment the prompt before feeding it to vectara
# text = "the user asks the following to his health adviser " + text
# 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)
client = OpenAI()
prompt ="Answer in the same language, write it better, more understandable and shorter:"
markdown_output_final = markdown_output
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": markdown_output_final}
]
)
final_response= completion.choices[0].message.content
return final_response
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(label="talk in french",
# 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: 
# #### 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?"],
# ],
# )
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:
#### 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)"
"""
with gr.Blocks(theme='ParityError/Anime') as iface :
gr.Markdown(welcome_message)
with gr.Tab("text summarization"):
text_input = gr.Textbox(label="input text",lines=5)
text_output = gr.Markdown(label="output text")
text_button = gr.Button("process text")
with gr.Tab("image identification"):
image_input = gr.Image(label="upload image")
image_output = gr.Markdown(label="output text")
image_button = gr.Button("process image")
with gr.Tab("speech to text translation"):
audio_input = gr.Audio(label="talk in french",
sources=["microphone"],type="filepath",)
audio_output = gr.Markdown(label="output text")
audio_button = gr.Button("process audio")
text_button.click(process_and_query, inputs=text_input, outputs=text_output)
image_button.click(process_image, inputs=image_input, outputs=image_output)
audio_button.click(process_speech, inputs=audio_input, outputs=audio_output)
iface.launch()