# Welcome to Team Tonic's MultiMed 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, ) from gradio_client import Client 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 import PIL dotenv.load_dotenv() client = Client("https://facebook-seamless-m4t.hf.space/--replicas/frq8b/") 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") 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" result = client.predict(task_name="S2TT", audio_source="microphone", input_audio_mic=sound, input_audio_file=None, input_text=None, source_language=None, target_language="English") print(result) return result[1] def process_speech_using_model(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 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 f"{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, 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') text = process_speech(audio) print(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) return markdown_output + text 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", type="filepath", sources="microphone", visible=True), ], 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: Duplicate Space #### 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()