Update app.py

app.py

@@ -1,40 +1,27 @@
 import gradio as gr
-import tensorflow as tf
-import pdfplumber
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import timm
-import torch
-import pandas as pd
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load pre-trained zero-shot model for text classification
-classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+# Load the PastelMed model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("harishussain12/PastelMed")
+model = AutoModelForCausalLM.from_pretrained("harishussain12/PastelMed")
 
-# Pre-trained ResNet50 model for X-ray or image analysis
-image_model = timm.create_model('resnet50', pretrained=True)
-image_model.eval()
-
-# Load saved TensorFlow eye disease detection model
-eye_model = tf.keras.models.load_model('model.h5')
-
-# Load NeuraMedAW model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/NeuraMedAW")
-model = AutoModelForCausalLM.from_pretrained("ahmed-7124/NeuraMedAW")
-
-# Patient database
+# Patient database (example, can be expanded)
 patients_db = []
 
-# Disease details for medical report analyzer
-disease_details = {
-    "anemia": {"medication": "Iron supplements", "precaution": "Eat iron-rich foods", "doctor": "Hematologist"},
-    "viral infection": {"medication": "Antiviral drugs", "precaution": "Stay hydrated", "doctor": "Infectious Disease Specialist"},
-    "liver disease": {"medication": "Hepatoprotective drugs", "precaution": "Avoid alcohol", "doctor": "Hepatologist"},
-    "diabetes": {"medication": "Metformin or insulin", "precaution": "Monitor sugar levels", "doctor": "Endocrinologist"},
-}
-
-# Passwords
-doctor_password = "doctor123"
+# Doctor's assistant function
+def doctor_assistant(question):
+    # Encode the input question
+    inputs = tokenizer(question, return_tensors="pt")
+    
+    # Generate a response from the model
+    outputs = model.generate(inputs["input_ids"], max_length=200, num_return_sequences=1)
+    
+    # Decode the generated response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
 
-# Functions
+# Function to register a patient (simplified version)
 def register_patient(name, age, gender, password):
     patient_id = len(patients_db) + 1
     patients_db.append({
@@ -50,81 +37,16 @@ def register_patient(name, age, gender, password):
     })
     return f"✅ Patient {name} registered successfully. Patient ID: {patient_id}"
 
-def analyze_report(patient_id, report_text):
-    candidate_labels = list(disease_details.keys())
-    result = classifier(report_text, candidate_labels)
-    diagnosis = result['labels'][0]
-
-    # Update patient's record
-    medication = disease_details[diagnosis]['medication']
-    precaution = disease_details[diagnosis]['precaution']
-    doctor = disease_details[diagnosis]['doctor']
-    for patient in patients_db:
-        if patient['ID'] == patient_id:
-            patient.update(Diagnosis=diagnosis, Medications=medication, Precautions=precaution, Doctor=doctor)
-    return f"🔍 Diagnosis: {diagnosis}"
-
-def extract_pdf_report(pdf):
-    text = ""
-    with pdfplumber.open(pdf.name) as pdf_file:
-        for page in pdf_file.pages:
-            text += page.extract_text()
-    return text
-
-def predict_eye_disease(input_image):
-    input_image = tf.image.resize(input_image, [224, 224]) / 255.0
-    input_image = tf.expand_dims(input_image, 0)
-    predictions = eye_model.predict(input_image)
-    labels = ['Cataract', 'Conjunctivitis', 'Glaucoma', 'Normal']
-    confidence_scores = {labels[i]: round(predictions[0][i] * 100, 2) for i in range(len(labels))}
-    if confidence_scores['Normal'] > 50:
-        return f"Congrats! No disease detected. Confidence: {confidence_scores['Normal']}%"
-    return "\n".join([f"{label}: {confidence}%" for label, confidence in confidence_scores.items()])
-
-def doctor_space(patient_id):
-    for patient in patients_db:
-        if patient["ID"] == patient_id:
-            return f"⚠️ Precautions: {patient['Precautions']}\n👩‍⚕️ Recommended Doctor: {patient['Doctor']}"
-    return "❌ Patient not found. Please check the ID."
-
-def pharmacist_space(patient_id):
-    for patient in patients_db:
-        if patient["ID"] == patient_id:
-            return f"💊 Medications: {patient['Medications']}"
-    return "❌ Patient not found. Please check the ID."
-
-def patient_dashboard(patient_id, password):
-    for patient in patients_db:
-        if patient["ID"] == patient_id and patient["Password"] == password:
-            return (f"🩺 Name: {patient['Name']}\n"
-                    f"📋 Diagnosis: {patient['Diagnosis']}\n"
-                    f"💊 Medications: {patient['Medications']}\n"
-                    f"⚠️ Precautions: {patient['Precautions']}\n"
-                    f"👩‍⚕️ Recommended Doctor: {patient['Doctor']}")
-    return "❌ Access Denied: Invalid ID or Password."
-
-def doctor_dashboard(password):
-    if password != doctor_password:
-        return "❌ Access Denied: Incorrect Password"
-    if not patients_db:
-        return "No patient records available."
-    details = []
-    for patient in patients_db:
-        details.append(f"🩺 Name: {patient['Name']}\n"
-                       f"📋 Diagnosis: {patient['Diagnosis']}\n"
-                       f"💊 Medications: {patient['Medications']}\n"
-                       f"⚠️ Precautions: {patient['Precautions']}\n"
-                       f"👩‍⚕️ Recommended Doctor: {patient['Doctor']}")
-    return "\n\n".join(details)
-
-def doctor_consultant(patient_query):
-    # Combine both models for a more comprehensive answer
-    inputs = tokenizer(patient_query, return_tensors="pt")
-    output = model.generate(**inputs, max_length=500)
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response
+# Gradio Interface for Doctor Assistance
+doctor_assistant_interface = gr.Interface(
+    fn=doctor_assistant,
+    inputs=gr.Textbox(label="Ask a Question to the Doctor Assistant"),
+    outputs="text",
+    title="Doctor Assistant",
+    description="Ask the assistant for medical advice and it will generate a response based on the PastelMed model."
+)
 
-# Gradio Interfaces
+# Gradio Interface for Patient Registration (for testing)
 registration_interface = gr.Interface(
     fn=register_patient,
     inputs=[
@@ -136,60 +58,6 @@ registration_interface = gr.Interface(
     outputs="text",
 )
 
-pdf_extraction_interface = gr.Interface(
-    fn=extract_pdf_report,
-    inputs=gr.File(label="Upload PDF Report"),
-    outputs="text",
-)
-
-report_analysis_interface = gr.Interface(
-    fn=analyze_report,
-    inputs=[
-        gr.Number(label="Patient ID"),
-        gr.Textbox(label="Report Text"),
-    ],
-    outputs="text",
-)
-
-eye_disease_interface = gr.Interface(
-    fn=predict_eye_disease,
-    inputs=gr.Image(label="Upload an Eye Image", type="numpy"),
-    outputs="text",
-)
-
-doctor_space_interface = gr.Interface(
-    fn=doctor_space,
-    inputs=gr.Number(label="Patient ID"),
-    outputs="text",
-)
-
-pharmacist_space_interface = gr.Interface(
-    fn=pharmacist_space,
-    inputs=gr.Number(label="Patient ID"),
-    outputs="text",
-)
-
-patient_dashboard_interface = gr.Interface(
-    fn=patient_dashboard,
-    inputs=[
-        gr.Number(label="Patient ID"),
-        gr.Textbox(label="Password", type="password"),
-    ],
-    outputs="text",
-)
-
-doctor_dashboard_interface = gr.Interface(
-    fn=doctor_dashboard,
-    inputs=gr.Textbox(label="Doctor Password", type="password"),
-    outputs="text",
-)
-
-doctor_consultant_interface = gr.Interface(
-    fn=doctor_consultant,
-    inputs=gr.Textbox(label="Ask the Doctor a Question"),
-    outputs="text",
-)
-
 # Gradio App Layout
 with gr.Blocks() as app:
     gr.Markdown("# Medico GPT")
@@ -197,28 +65,7 @@ with gr.Blocks() as app:
     with gr.Tab("Patient Registration"):
         registration_interface.render()
     
-    with gr.Tab("Analyze Medical Report"):
-        report_analysis_interface.render()
-    
-    with gr.Tab("Extract PDF Report"):
-        pdf_extraction_interface.render()
-    
-    with gr.Tab("Ophthalmologist Space"):
-        eye_disease_interface.render()
-    
-    with gr.Tab("Doctor Space"):
-        doctor_space_interface.render()
-    
-    with gr.Tab("Pharmacist Space"):
-        pharmacist_space_interface.render()
-    
-    with gr.Tab("Patient Dashboard"):
-        patient_dashboard_interface.render()
-    
-    with gr.Tab("Doctor Dashboard"):
-        doctor_dashboard_interface.render()
-
-    with gr.Tab("Doctor Consultant"):
-        doctor_consultant_interface.render()
+    with gr.Tab("Doctor Assistant"):
+        doctor_assistant_interface.render()
 
 app.launch(share=True)