feat: change form

app.py

@@ -3,6 +3,8 @@ import numpy as np
 import gradio as gr
 import requests
 import json
+from transformers import ViTImageProcessor, ViTModel
+from PIL import Image
 
 # Store the server's URL
 SERVER_URL = "https://ppaihack-match.azurewebsites.net/"
@@ -25,7 +27,7 @@ additional_categories = {
     "Ethnicity": ["White", "Black or African American", "Asian", "American Indian or Alaska Native", "Native Hawaiian or Other Pacific Islander", "Other"],
     "Geographic_Location": ["North America", "South America", "Europe", "Asia", "Africa", "Australia", "Antarctica"],
     "Smoking_Status": ["Never", "Former", "Current"],
-    "Diagnoses_ICD10": ["E11.9", "I10", "J45.909", "M54.5", "F32.9", "K21.9"],
+    "Diagnoses_ICD10": ["Actinic keratosis", "Melanoma", "Dermatofibroma", "Vascular lesion","None"],
     "Medications": ["Metformin", "Lisinopril", "Atorvastatin", "Amlodipine", "Omeprazole", "Simvastatin", "Levothyroxine", "None"],
     "Allergies": ["Penicillin", "Peanuts", "Shellfish", "Latex", "Bee stings", "None"],
     "Previous_Treatments": ["Chemotherapy", "Radiation Therapy", "Surgery", "Physical Therapy", "Immunotherapy", "None"],
@@ -41,7 +43,6 @@ age_input = gr.Slider(minimum=18, maximum=100, label="Age ", step=1)
 gender_input = gr.Radio(choices=additional_categories["Gender"], label="Gender")
 ethnicity_input = gr.Radio(choices=additional_categories["Ethnicity"], label="Ethnicity")
 geographic_location_input = gr.Radio(choices=additional_categories["Geographic_Location"], label="Geographic Location")
-diagnoses_icd10_input = gr.CheckboxGroup(choices=additional_categories["Diagnoses_ICD10"], label="Diagnoses (ICD-10)")
 medications_input = gr.CheckboxGroup(choices=additional_categories["Medications"], label="Medications")
 allergies_input = gr.CheckboxGroup(choices=additional_categories["Allergies"], label="Allergies")
 previous_treatments_input = gr.CheckboxGroup(choices=additional_categories["Previous_Treatments"], label="Previous Treatments")
@@ -213,17 +214,54 @@ def process_patient_data(age, gender, ethnicity, geographic_location, diagnoses_
             # f"Encrypted data: {encrypted_array}",
             f"Decrypted result: {response.json()}"
         )
+# Define the function to handle image upload
+def handle_image_upload(image):
+    url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+    image = Image.open(requests.get(url, stream=True).raw)
+
+    processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
+    model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+    inputs = processor(images=image, return_tensors="pt")
+
+    outputs = model(**inputs)
+    pooler_output = outputs.pooler_output[0]
+    sclaed_output = 127 + 127 * pooler_output / pooler_output.abs().max()
+    sclaed_output = sclaed_output.to(int)
+    return ["Melanoma", "Vascular lesion"]
 
 # Create the Gradio interface
-demo = gr.Interface(
-    fn=process_patient_data, 
-    inputs=[
-        age_input, gender_input, ethnicity_input, geographic_location_input, diagnoses_icd10_input, medications_input, allergies_input, previous_treatments_input, blood_glucose_level_input, blood_pressure_systolic_input, blood_pressure_diastolic_input, bmi_input, smoking_status_input, alcohol_consumption_input, exercise_habits_input, diet_input, condition_severity_input, functional_status_input, previous_trial_participation_input
-    ], 
-    outputs="text",
-    title="Patient Data Criteria Form",
-    description="Please fill in the criteria for the type of patients you are looking for."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# Patient Data Criteria Form\nPlease fill in the criteria for the type of patients you are looking for.")
+    with gr.Column():
+        with gr.Group():
+            age_input.render()
+            gender_input.render()
+            ethnicity_input.render()
+            geographic_location_input.render()
+            medications_input.render()
+            allergies_input.render()
+            previous_treatments_input.render()
+            blood_glucose_level_input.render()
+            blood_pressure_systolic_input.render()
+            blood_pressure_diastolic_input.render()
+            bmi_input.render()
+            smoking_status_input.render()
+            alcohol_consumption_input.render()
+            exercise_habits_input.render()
+            diet_input.render()
+            condition_severity_input.render()
+            functional_status_input.render()
+            previous_trial_participation_input.render()
+        with gr.Group():
+            diagnoses_icd10_input = gr.CheckboxGroup(choices=additional_categories["Diagnoses_ICD10"], label="Skin Diagnosis", interactive=False)
+            image_input = gr.Image(label="Upload an Image")
+            gr.Button("Upload").click(handle_image_upload, inputs=image_input, outputs=diagnoses_icd10_input)
+        with gr.Group():
+            output = gr.JSON(label="Patient Data JSON")
+            gr.Button("Submit").click(process_patient_data, inputs=[
+                age_input, gender_input, ethnicity_input, geographic_location_input, diagnoses_icd10_input, medications_input, allergies_input, previous_treatments_input, blood_glucose_level_input, blood_pressure_systolic_input, blood_pressure_diastolic_input, bmi_input, smoking_status_input, alcohol_consumption_input, exercise_habits_input, diet_input, condition_severity_input, functional_status_input, previous_trial_participation_input
+            ], outputs=output)
+
 
 # Launch the app
 demo.launch()