Update app.py

app.py

@@ -47,7 +47,6 @@ def train_model(output_range):
     model = LinearRegression().fit(X, y)
     return model
 
-
 # Load models
 try:
     hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
@@ -160,7 +159,7 @@ def save_results_to_pdf(test_results, filename):
         return f"Error saving PDF: {str(e)}", None
 
 # Build health card layout
-def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id=""):
+def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id="", pdf_filepath=""):
     from datetime import datetime
     current_date = datetime.now().strftime("%B %d, %Y")
     
@@ -224,121 +223,31 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
 # Initialize global variable for patient details
 current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}
 
-# Modified analyze_face function
-def analyze_face(input_data):
-    if isinstance(input_data, str):  # Video input (file path in Replit)
-        cap = cv2.VideoCapture(input_data)
-        if not cap.isOpened():
-            return "<div style='color:red;'>⚠️ Error: Could not open video.</div>", None
-        ret, frame = cap.read()
-        cap.release()
-        if not ret:
-            return "<div style='color:red;'>⚠️ Error: Could not read video frame.</div>", None
-    else:  # Image input
-        frame = input_data
-        if frame is None:
-            return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
-
-    # Resize image to reduce processing time
-    frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
-    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(frame_rgb)
-    if not result.multi_face_landmarks:
-        return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
-    landmarks = result.multi_face_landmarks[0].landmark  # Fixed: Use integer index
-    features = extract_features(frame_rgb, landmarks)
-    test_values = {}
-    r2_scores = {}
-
-    for label in models:
-        if label == "Hemoglobin":
-            prediction = models[label].predict([features])[0]
-            test_values[label] = prediction
-            r2_scores[label] = 0.385
-        else:
-            value = models[label].predict([[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
-            test_values[label] = value
-            r2_scores[label] = 0.0
-
-    gray = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)
-    green_std = np.std(frame_rgb[:, :, 1]) / 255
-    brightness_std = np.std(gray) / 255
-    tone_index = np.mean(frame_rgb[100:150, 100:150]) / 255 if frame_rgb[
-        100:150, 100:150].size else 0.5
-    hr_features = [brightness_std, green_std, tone_index]
-    heart_rate = float(np.clip(hr_model.predict([hr_features])[0], 60, 100))
-    skin_patch = frame_rgb[100:150, 100:150]
-    skin_tone_index = np.mean(skin_patch) / 255 if skin_patch.size else 0.5
-    brightness_variation = np.std(cv2.cvtColor(frame_rgb,
-                                               cv2.COLOR_RGB2GRAY)) / 255
-    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
-    spo2 = spo2_model.predict([spo2_features])[0]
-    rr = int(12 + abs(heart_rate % 5 - 2))
-
-    test_results = {
-        "Hematology":
-        build_table("🩸 Hematology",
-                    [("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)),
-                     ("WBC Count", test_values["WBC Count"], (4.0, 11.0)),
-                     ("Platelet Count", test_values["Platelet Count"],
-                      (150, 450))]),
-        "Iron Panel":
-        build_table("🧬 Iron Panel",
-                    [("Iron", test_values["Iron"], (60, 170)),
-                     ("Ferritin", test_values["Ferritin"], (30, 300)),
-                     ("TIBC", test_values["TIBC"], (250, 400))]),
-        "Liver & Kidney":
-        build_table("🧬 Liver & Kidney",
-                    [("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)),
-                     ("Creatinine", test_values["Creatinine"], (0.6, 1.2)),
-                     ("Urea", test_values["Urea"], (7, 20))]),
-        "Electrolytes":
-        build_table("🧪 Electrolytes",
-                    [("Sodium", test_values["Sodium"], (135, 145)),
-                     ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
-        "Vitals":
-        build_table("❤️ Vitals",
-                    [("SpO2", spo2, (95, 100)),
-                     ("Heart Rate", heart_rate, (60, 100)),
-                     ("Temperature", test_values["Temperature"], (97, 99)),
-                     ("BP Systolic", test_values["BP Systolic"], (90, 120)),
-                     ("BP Diastolic", test_values["BP Diastolic"], (60, 80))])
-    }
-
-    summary = "<ul><li>Your hemoglobin is a bit low — this could mean mild anemia.</li><li>Low iron storage detected — consider an iron profile test.</li><li>Elevated bilirubin — possible jaundice. Recommend LFT.</li><li>High HbA1c — prediabetes indication. Recommend glucose check.</li><li>Low SpO₂ — suggest retesting with a pulse oximeter.</li></ul>"
-
-    _, buffer = cv2.imencode('.png', frame_rgb)
-    profile_image_base64 = base64.b64encode(buffer).decode('utf-8')
-
-    # Use global patient details
-    global current_patient_details
-    health_card_html = build_health_card(
-        profile_image_base64, 
-        test_results,
-        summary, 
-        current_patient_details['name'],
-        current_patient_details['age'],
-        current_patient_details['gender'],
-        current_patient_details['id']
-    )
+# Route the inputs to the correct function
+def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id):
+    if mode == "Image" and image is None:
+        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+    if mode == "Video" and video is None:
+        return "<div style='color:red;'>⚠️ Error: No video provided.</div>", None
     
-    # Generate PDF and return for download
-    pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
-    pdf_result, pdf_filepath = save_results_to_pdf(test_results, pdf_filename)
+    # Store patient details globally for use in analyze_face
+    global current_patient_details
+    current_patient_details = {
+        'name': patient_name,
+        'age': patient_age, 
+        'gender': patient_gender,
+        'id': patient_id
+    }
     
-    if pdf_filepath:
-        # Copy the PDF to a temporary directory for Gradio to serve it
-        temp_pdf_path = "/tmp/" + os.path.basename(pdf_filepath)
-        shutil.copy(pdf_filepath, temp_pdf_path)
-        
-        return health_card_html, temp_pdf_path
+    health_card_html, pdf_file_path = analyze_face(image if mode == "Image" else video)
+    return health_card_html, pdf_file_path
 
 
 # Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("""# 🧠 Face-Based Lab Test AI Report (Video Mode)""")
     with gr.Row():
-        with gr.Column():
+        with gr.Column(elem_id="input-container"):
             gr.Markdown("### Patient Information")
             patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
             patient_age = gr.Number(label="Age", value=25, minimum=1, maximum=120)
@@ -353,10 +262,13 @@ with gr.Blocks() as demo:
             video_input = gr.Video(label="Upload Face Video",
                                    sources=["upload", "webcam"])
             submit_btn = gr.Button("🔍 Analyze")
-        with gr.Column():
+        with gr.Column(elem_id="output-container"):
             result_html = gr.HTML(label="🧪 Health Report Table")
             result_pdf = gr.File(label="Download Health Report PDF", interactive=False)
 
-           
+            submit_btn.click(fn=route_inputs,
+                             inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
+                             outputs=[result_html, result_pdf])
+
 # Launch Gradio for Replit
 demo.launch(server_name="0.0.0.0", server_port=7860)