Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import tensorflow as tf
 import pdfplumber
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import timm
 import torch
 import pandas as pd
@@ -16,6 +16,10 @@ image_model.eval()
 # Load saved TensorFlow eye disease detection model
 eye_model = tf.keras.models.load_model('model.h5')
 
+# Load doctor consultation model
+doctor_tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/dgptAW")
+doctor_model = AutoModelForCausalLM.from_pretrained("ahmed-7124/dgptAW")
+
 # Patient database
 patients_db = []
 
@@ -44,7 +48,7 @@ def register_patient(name, age, gender, password):
         "Precautions": "",
         "Doctor": ""
     })
-    return f"✅ Patient {name} registered successfully. Patient ID: {patient_id}"
+    return f"\u2705 Patient {name} registered successfully. Patient ID: {patient_id}"
 
 def analyze_report(patient_id, report_text):
     candidate_labels = list(disease_details.keys())
@@ -58,7 +62,7 @@ def analyze_report(patient_id, report_text):
     for patient in patients_db:
         if patient['ID'] == patient_id:
             patient.update(Diagnosis=diagnosis, Medications=medication, Precautions=precaution, Doctor=doctor)
-    return f"🔍 Diagnosis: {diagnosis}"
+    return f"\ud83d\udd0d Diagnosis: {diagnosis}"
 
 def extract_pdf_report(pdf):
     text = ""
@@ -80,39 +84,45 @@ def predict_eye_disease(input_image):
 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."
+            return f"\u26a0 Precautions: {patient['Precautions']}\n\ud83d\udc69\u200d\u2695 Recommended Doctor: {patient['Doctor']}"
+    return "\u274c 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."
+            return f"\ud83d\udc8a Medications: {patient['Medications']}"
+    return "\u274c 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."
+            return (f"\ud83e\ude7a Name: {patient['Name']}\n"
+                    f"\ud83d\udccb Diagnosis: {patient['Diagnosis']}\n"
+                    f"\ud83d\udc8a Medications: {patient['Medications']}\n"
+                    f"\u26a0 Precautions: {patient['Precautions']}\n"
+                    f"\ud83d\udc69\u200d\u2695 Recommended Doctor: {patient['Doctor']}")
+    return "\u274c Access Denied: Invalid ID or Password."
 
 def doctor_dashboard(password):
     if password != doctor_password:
-        return "❌ Access Denied: Incorrect Password"
+        return "\u274c 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']}")
+        details.append(f"\ud83e\ude7a Name: {patient['Name']}\n"
+                       f"\ud83d\udccb Diagnosis: {patient['Diagnosis']}\n"
+                       f"\ud83d\udc8a Medications: {patient['Medications']}\n"
+                       f"\u26a0 Precautions: {patient['Precautions']}\n"
+                       f"\ud83d\udc69\u200d\u2695 Recommended Doctor: {patient['Doctor']}")
     return "\n\n".join(details)
 
+def doctor_consult(query):
+    inputs = doctor_tokenizer.encode(query, return_tensors="pt")
+    outputs = doctor_model.generate(inputs, max_length=200, num_return_sequences=1, pad_token_id=doctor_tokenizer.eos_token_id)
+    response = doctor_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
 # Gradio Interfaces
 registration_interface = gr.Interface(
     fn=register_patient,
@@ -173,32 +183,41 @@ doctor_dashboard_interface = gr.Interface(
     outputs="text",
 )
 
+doctor_consult_interface = gr.Interface(
+    fn=doctor_consult,
+    inputs=gr.Textbox(label="Enter your query for the doctor"),
+    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()
 
-app.launch(share=True)
+    with gr.Tab("Doctor Consult"):
+        doctor_consult_interface.render()
+
+app.launch(share=True)