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from fastai.text.all import *
from transformers import AutoModelForSequenceClassification, AutoTokenizer, BlenderbotForConditionalGeneration, BlenderbotTokenizer
import torch
import gradio as gr
# Load the medical model
medical_learn = load_learner('model.pkl')
# Medical model configuration
medical_description = "Medical Diagnosis"
medical_categories = ['Allergy', 'Anemia', 'Bronchitis', 'Diabetes', 'Diarrhea', 'Fatigue', 'Flu', 'Malaria', 'Stress']
def classify_medical_text(txt):
pred, idx, probs = medical_learn.predict(txt)
return dict(zip(medical_categories, map(float, probs)))
# Load the psychiatric model from Hugging Face
psychiatric_model_name = "nlp4good/psych-search" # Replace with the appropriate model
psychiatric_tokenizer = AutoTokenizer.from_pretrained(psychiatric_model_name)
psychiatric_model = AutoModelForSequenceClassification.from_pretrained(psychiatric_model_name)
# Psychiatric model configuration
psychiatric_description = "Psychiatric Analysis"
psychiatric_labels = ['Depression', 'Anxiety', 'Bipolar Disorder', 'PTSD', 'OCD', 'Stress', 'Schizophrenia'] # Adjust based on the model
def classify_psychiatric_text(txt):
inputs = psychiatric_tokenizer(txt, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = psychiatric_model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1).squeeze().tolist()
return dict(zip(psychiatric_labels, probabilities))
# Load BlenderBot for Lifestyle and Nutrition Chatbot
blender_model_name = "facebook/blenderbot-3B" # Pre-trained BlenderBot 3B model
blender_tokenizer = BlenderbotTokenizer.from_pretrained(blender_model_name)
blender_model = BlenderbotForConditionalGeneration.from_pretrained(blender_model_name)
# Chat function for Lifestyle and Nutrition
chat_history = []
def chatbot_response(user_input):
global chat_history
new_input_ids = blender_tokenizer.encode(user_input + blender_tokenizer.eos_token, return_tensors='pt')
bot_input_ids = torch.cat([chat_history, new_input_ids], dim=-1) if chat_history else new_input_ids
chat_history = blender_model.generate(bot_input_ids, max_length=1000, pad_token_id=blender_tokenizer.eos_token_id)
response = blender_tokenizer.decode(chat_history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
return response
def clear_chat():
global chat_history
chat_history = []
return []
# Gradio Interfaces
medical_text = gr.Textbox(lines=2, label='Describe your symptoms in detail')
medical_label = gr.Label()
medical_examples = ['I feel short of breath and have a high fever.', 'My throat hurts and I keep sneezing.', 'I am always thirsty.']
psychiatric_text = gr.Textbox(lines=2, label='Describe your mental health concerns in detail')
psychiatric_label = gr.Label()
psychiatric_examples = ['I feel hopeless and have no energy.', 'I am unable to concentrate and feel anxious all the time.', 'I have recurring intrusive thoughts.']
lifestyle_chatbot = gr.Chatbot(label="Chat with me about diet and nutrition!")
lifestyle_msg = gr.Textbox(placeholder="Ask your question here...", label="Your Question")
lifestyle_clear = gr.Button("Clear Chat")
def user_message(input_text):
if not input_text.strip():
return lifestyle_chatbot, "Please enter a question."
response = chatbot_response(input_text)
lifestyle_chatbot.append((input_text, response))
return lifestyle_chatbot, ""
# Lifestyle & Nutrition Interface
lifestyle_interface = gr.Interface(
fn=user_message,
inputs=[lifestyle_msg],
outputs=[lifestyle_chatbot, lifestyle_msg],
live=True,
title="Nutritionist Chatbot",
description="Ask me anything about diet, food, and nutrition!"
)
# Medical Diagnosis Interface
medical_interface = gr.Interface(
fn=classify_medical_text,
inputs=medical_text,
outputs=medical_label,
examples=medical_examples,
description=medical_description,
)
# Psychiatric Analysis Interface
psychiatric_interface = gr.Interface(
fn=classify_psychiatric_text,
inputs=psychiatric_text,
outputs=psychiatric_label,
examples=psychiatric_examples,
description=psychiatric_description,
)
# Combine interfaces using Tabs
app = gr.TabbedInterface(
[medical_interface, psychiatric_interface, lifestyle_interface],
["Medical Diagnosis", "Psychiatric Analysis", "Lifestyle & Nutrition Chat"]
)
app.launch(inline=False)
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