Update app.py

app.py

@@ -36,12 +36,12 @@ doctor_password = "doctor123"
 # Loading the custom model for consultation with the doctor
 try:
     # Force using the slow tokenizer for compatibility
-    tokenizer = AutoTokenizer.from_pretrained("harishussain12/PastelMed", use_fast=False)
+    tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/NeuraMedAW", use_fast=False)
 except Exception as e:
     print(f"Tokenizer error: {e}")
-    tokenizer = AutoTokenizer.from_pretrained("harishussain12/PastelMed", use_fast=False)
+    tokenizer = AutoTokenizer.from_pretrained("ahmed-7124/NeuraMedAW", use_fast=False)
 
-model = AutoModelForCausalLM.from_pretrained("harishussain12/PastelMed")
+model = AutoModelForCausalLM.from_pretrained("ahmed-7124/NeuraMedAW")
 
 def consult_doctor(prompt):
     inputs = tokenizer(prompt, return_tensors="pt")
@@ -66,7 +66,16 @@ def register_patient(name, age, gender, password):
     })
     return f"✅ Patient {name} registered successfully. Patient ID: {patient_id}"
 
-def analyze_report(patient_id, report_text):
+def analyze_or_extract_report(patient_id, pdf=None, report_text=None):
+    if pdf:
+        # Extract text from PDF
+        with pdfplumber.open(pdf.name) as pdf_file:
+            report_text = "".join([page.extract_text() for page in pdf_file.pages])
+    
+    if not report_text:
+        return "❌ Please provide a report text or upload a PDF."
+
+    # Analyze the report
     candidate_labels = list(disease_details.keys())
     result = classifier(report_text, candidate_labels)
     diagnosis = result['labels'][0]
@@ -78,14 +87,15 @@ 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"🔍 Diagnosis: {diagnosis}\n💊 Medication: {medication}\n⚠ Precaution: {precaution}\n👩‍⚕ Recommended Doctor: {doctor}"
 
-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 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
@@ -151,11 +161,22 @@ pdf_extraction_interface = gr.Interface(
     outputs="text",
 )
 
-report_analysis_interface = gr.Interface(
-    fn=analyze_report,
-    inputs=[ 
+# report_analysis_interface = gr.Interface(
+#     fn=analyze_report,
+#     inputs=[ 
+#         gr.Number(label="Patient ID"),
+#         gr.Textbox(label="Report Text"),
+#     ],
+#     outputs="text",
+# )
+
+# Unified Gradio Interface
+analyze_report_interface = gr.Interface(
+    fn=analyze_or_extract_report,
+    inputs=[
         gr.Number(label="Patient ID"),
-        gr.Textbox(label="Report Text"),
+        gr.File(label="Upload PDF Report", optional=True),
+        gr.Textbox(label="Report Text (Optional)", optional=True),
     ],
     outputs="text",
 )
@@ -206,8 +227,10 @@ 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("Analyze Medical Report"):
+    #     report_analysis_interface.render()
+     with gr.Tab("Analyze Medical Report"):
+        analyze_report_interface.render()
     
     with gr.Tab("Extract PDF Report"):
         pdf_extraction_interface.render()