Update app.py

app.py

@@ -16,10 +16,6 @@ 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 = []
 
@@ -34,6 +30,21 @@ disease_details = {
 # Passwords
 doctor_password = "doctor123"
 
+# Load doctor consultation model and tokenizer
+try:
+    doctor_tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/dgptAW")
+except Exception as e:
+    print(f"Fast tokenizer failed: {e}. Falling back to slow tokenizer.")
+    doctor_tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/dgptAW", use_fast=False)
+
+doctor_model = AutoModelForCausalLM.from_pretrained("ahmed-7124/dgptAW")
+
+def consult_doctor(prompt):
+    inputs = doctor_tokenizer(prompt, return_tensors="pt")
+    outputs = doctor_model.generate(**inputs, max_new_tokens=100)
+    response = doctor_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
 # Functions
 def register_patient(name, age, gender, password):
     patient_id = len(patients_db) + 1
@@ -48,7 +59,7 @@ def register_patient(name, age, gender, password):
         "Precautions": "",
         "Doctor": ""
     })
-    return f"\u2705 Patient {name} registered successfully. Patient ID: {patient_id}"
+    return f"✅ Patient {name} registered successfully. Patient ID: {patient_id}"
 
 def analyze_report(patient_id, report_text):
     candidate_labels = list(disease_details.keys())
@@ -62,7 +73,7 @@ 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"\ud83d\udd0d Diagnosis: {diagnosis}"
+    return f"🔍 Diagnosis: {diagnosis}"
 
 def extract_pdf_report(pdf):
     text = ""
@@ -84,45 +95,39 @@ def predict_eye_disease(input_image):
 def doctor_space(patient_id):
     for patient in patients_db:
         if patient["ID"] == patient_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."
+            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"\ud83d\udc8a Medications: {patient['Medications']}"
-    return "\u274c Patient not found. Please check the 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"\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."
+            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 "\u274c Access Denied: Incorrect Password"
+        return "❌ Access Denied: Incorrect Password"
     if not patients_db:
         return "No patient records available."
     details = []
     for patient in patients_db:
-        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']}")
+        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_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,
@@ -183,41 +188,41 @@ 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"),
+consult_doctor_interface = gr.Interface(
+    fn=consult_doctor,
+    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()
 
     with gr.Tab("Doctor Consult"):
-        doctor_consult_interface.render()
+        consult_doctor_interface.render()
 
 app.launch(share=True)