# 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 from transformers import AutoTokenizer, MistralForCausalLM import torch import random from textwrap import wrap import transformers from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM from peft import PeftModel, PeftConfig import torch import os # Global variables to hold component references components = {} dotenv.load_dotenv() seamless_client = Client("facebook/seamless_m4t") HuggingFace_Token = os.getenv("HuggingFace_Token") hf_token = os.getenv("HuggingFace_Token") base_model_id = os.getenv('BASE_MODEL_ID', 'default_base_model_id') model_directory = os.getenv('MODEL_DIRECTORY', 'default_model_directory') device = "cuda" if torch.cuda.is_available() else "cpu" 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"**hallucination 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}" # Functions to Wrap the Prompt Correctly def wrap_text(text, width=90): lines = text.split('\n') wrapped_lines = [textwrap.fill(line, width=width) for line in lines] wrapped_text = '\n'.join(wrapped_lines) return wrapped_text def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"): # Combine user input and system prompt formatted_input = f"{user_input}{system_prompt}" # Encode the input text encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False) model_inputs = encodeds.to(device) # Generate a response using the model output = model.generate( **model_inputs, max_length=max_length, use_cache=True, early_stopping=True, bos_token_id=model.config.bos_token_id, eos_token_id=model.config.eos_token_id, pad_token_id=model.config.eos_token_id, temperature=0.1, do_sample=True ) # Decode the response response_text = tokenizer.decode(output[0], skip_special_tokens=True) return response_text # Instantiate the Tokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True, padding_side="left") # tokenizer = AutoTokenizer.from_pretrained("Tonic/stablemed", trust_remote_code=True, padding_side="left") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = 'left' # Load the PEFT model peft_config = PeftConfig.from_pretrained("Tonic/stablemed", token=hf_token) peft_model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True) peft_model = PeftModel.from_pretrained(peft_model, "Tonic/stablemed", token=hf_token) class ChatBot: def __init__(self): self.history = [] def predict(self, user_input, system_prompt="You are an expert medical analyst:"): formatted_input = f"{system_prompt}{user_input}" user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt") response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) response_text = tokenizer.decode(response[0], skip_special_tokens=True) return response_text bot = ChatBot() def process_summary_with_stablemed(summary): system_prompt = "You are a medical instructor . 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." response_text = bot.predict(summary, system_prompt) return response_text # Main function to handle the Gradio interface logic 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) combined_text += "\n\n**Audio Input:**\n" + audio_text # Process image input if image_input is not None: image_text = process_image(image_input) combined_text += "\n\n**Image Input:**\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) # Parse the Vectara response vectara_response = json.loads(vectara_response_json) summary = vectara_response.get('summary', 'No summary available') sources_info = vectara_response.get('sources', []) # Format Vectara response in Markdown markdown_output = "### Vectara Response Summary\n" markdown_output += f"* **Summary**: {summary}\n" markdown_output += "### Sources Information\n" for source in sources_info: markdown_output += f"* {source}\n" # Append the original image description in Markdown if image_description: markdown_output += "\n### Original Image Description\n" markdown_output += image_description + "\n" # Process the summary with OpenAI final_response = process_summary_with_stablemed(summary) # Evaluate hallucination hallucination_label = evaluate_hallucination(final_response, summary) # Add final response and hallucination label to Markdown output markdown_output += "\n### Processed Summary with StableMed\n" markdown_output += final_response + "\n" markdown_output += "\n### Hallucination Evaluation\n" markdown_output += f"* **Label**: {hallucination_label}\n" return markdown_output, hallucination_label except Exception as e: return f"Error occurred during processing: {e}", "No hallucination evaluation" welcome_message = """ # 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷 🗣️📝 This is an educational and accessible conversational tool. ### How To Use ⚕🗣️😷MultiMed⚕: 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using image, audio or text! 📚🌟💼 that uses [Tonic/stablemed](https://huggingface.co/Tonic/stablemed) and [adept/fuyu-8B](https://huggingface.co/adept/fuyu-8b) with [Vectara](https://huggingface.co/vectara) embeddings + retrieval. 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)" """ 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" ] def clear(): # Return default values return "None", None, None, "", [], [], [] def create_interface(): with gr.Blocks(theme='ParityError/Anime') as iface: # Display the welcome message gr.Markdown(welcome_message) # Add a 'None' or similar option to represent no selection input_language_options = ["None"] + languages input_language = gr.Dropdown(input_language_options, label="Select the language", value="English", interactive=True) with gr.Accordion("Use Voice", open=False) as voice_accordion: audio_input = gr.Audio(label="Speak", type="filepath", sources="microphone", examples=["audio1.wav", "audio2.mp3"]) audio_output = gr.Markdown(label="Output text") # Markdown component for audio with gr.Accordion("Use a Picture", open=False) as picture_accordion: image_input = gr.Image(label="Upload image", examples=["image1.jpg", "image2.png", "image3.jpg"]) image_output = gr.Markdown(label="Output text") # Markdown component for image with gr.Accordion("MultiMed", open=False) as multimend_accordion: text_input = gr.Textbox(label="Use Text", lines=3, placeholder="I have had a sore throat and phlegm for a few days and now my cough has gotten worse!") text_output = gr.Markdown(label="Output text") # Markdown component for text text_button = gr.Button("Use MultiMed") text_button.click(process_and_query, inputs=[input_language, audio_input, image_input, text_input], outputs=[text_output, hallucination_output]) clear_button = gr.Button("Clear") clear_button.click(clear, inputs=[], outputs=[input_language, audio_input, image_input, text_input]) return iface iface = create_interface() iface.launch(show_error=True, debug=True)