Update app.py

app.py

@@ -1,9 +1,10 @@
-# Import necessary libraries
 import gradio as gr
 import tensorflow as tf
 import pdfplumber
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import timm
+import torch
+import pandas as pd
 
 # Load pre-trained zero-shot model for text classification
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -15,6 +16,10 @@ 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
 patients_db = []
 
@@ -26,57 +31,194 @@ disease_details = {
     "diabetes": {"medication": "Metformin or insulin", "precaution": "Monitor sugar levels", "doctor": "Endocrinologist"},
 }
 
-# Doctor consultant models
-neura_tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/NeuraMedAW")
-neura_model = AutoModelForCausalLM.from_pretrained("ahmed-7124/NeuraMedAW")
-lynx_tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/LynxMedAW")
-lynx_model = AutoModelForCausalLM.from_pretrained("ahmed-7124/LynxMedAW")
-
 # Passwords
 doctor_password = "doctor123"
 
-# Helper Functions
-def generate_consultation_response(prompt):
-    neura_input = neura_tokenizer(prompt, return_tensors="pt")
-    neura_response = neura_model.generate(**neura_input, max_length=200, num_return_sequences=1)
-    neura_output = neura_tokenizer.decode(neura_response[0], skip_special_tokens=True)
+# Functions
+def register_patient(name, age, gender, password):
+    patient_id = len(patients_db) + 1
+    patients_db.append({
+        "ID": patient_id,
+        "Name": name,
+        "Age": age,
+        "Gender": gender,
+        "Password": password,
+        "Diagnosis": "",
+        "Medications": "",
+        "Precautions": "",
+        "Doctor": ""
+    })
+    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."
 
-    lynx_input = lynx_tokenizer(prompt, return_tensors="pt")
-    lynx_response = lynx_model.generate(**lynx_input, max_length=200, num_return_sequences=1)
-    lynx_output = lynx_tokenizer.decode(lynx_response[0], skip_special_tokens=True)
+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."
 
-    return f"**NeuraMedAW Response:**\n{neura_output}\n\n**LynxMedAW Response:**\n{lynx_output}"
+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)
 
-# Remaining helper functions are unchanged...
-# Register patient, analyze reports, extract PDF, predict eye disease, doctor space, pharmacist space, and dashboards.
+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 Interfaces
 registration_interface = gr.Interface(
     fn=register_patient,
-    inputs=[gr.Textbox(label="Patient Name"), gr.Number(label="Age"), gr.Radio(label="Gender", choices=["Male", "Female", "Other"]), gr.Textbox(label="Set Password", type="password")],
+    inputs=[
+        gr.Textbox(label="Patient Name"),
+        gr.Number(label="Age"),
+        gr.Radio(label="Gender", choices=["Male", "Female", "Other"]),
+        gr.Textbox(label="Set Password", type="password"),
+    ],
+    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",
 )
 
-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 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=generate_consultation_response, inputs=gr.Textbox(label="Enter Symptoms or Query"), 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")
-    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("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()
 
 app.launch(share=True)