app.py

import gradio as gr
from transformers import pipeline

# Load models
emotion_model = pipeline("text-classification", model="bert-base-uncased")
microbiome_model = pipeline("text-generation", model="microsoft/BioGPT-Large")
retina_model = pipeline("image-classification", model="microsoft/resnet-50")

# Define functions
def diagnose_emotion(text):
    return emotion_model(text)

def analyze_microbiome(symptoms):
    return microbiome_model(symptoms)

def analyze_retina(image):
    return retina_model(image)

# Gradio UI
with gr.Blocks() as app:
    gr.Markdown("# Diagnosify-AI - AI Medical Assistant")
    text_input = gr.Textbox(label="Enter Symptoms")
    image_input = gr.Image(type="pil", label="Upload Retina Scan")

    btn1 = gr.Button("Diagnose Emotion-based Disease")
    btn2 = gr.Button("Analyze Gut Health")
    btn3 = gr.Button("Detect Retinal Disease")

    output1 = gr.Textbox(label="Diagnosis")
    output2 = gr.Textbox(label="Microbiome Analysis")
    output3 = gr.Label(label="Retinal Disease Prediction")

    btn1.click(diagnose_emotion, inputs=text_input, outputs=output1)
    btn2.click(analyze_microbiome, inputs=text_input, outputs=output2)
    btn3.click(analyze_retina, inputs=image_input, outputs=output3)

# Launch the app
app.launch()