Update app.py

app.py

@@ -1,106 +1,111 @@
-import gradio as gr
-import cv2
-import numpy as np
-from PIL import Image
-import os
-from utils.model_loader import load_blood_group_model, load_gender_model
-from utils.predictor import predict_blood_group, predict_gender
-from utils.preprocessing import preprocess_fingerprint
-
-blood_model = None
-gender_model = None
-
-
-def load_models():
-    global blood_model, gender_model
-    if blood_model is None:
-        try:
-            blood_model = load_blood_group_model()
-        except Exception as e:
-            print(f"Warning: Blood group model not loaded: {e}")
-            blood_model = None
-
-    if gender_model is None:
-        try:
-            gender_model = load_gender_model()
-        except Exception as e:
-            print(f"Warning: Gender model not loaded: {e}")
-            gender_model = None
-
-    return blood_model, gender_model
-
-
-def analyze_fingerprint(image):
-    """Analyze uploaded fingerprint image"""
-    if image is None:
-        return "Please upload a fingerprint image.", "", "", ""
-    
-    try:
-        # Load models lazily
-        blood_model, gender_model = load_models()
-        if blood_model is None or gender_model is None:
-            missing = []
-            if blood_model is None:
-                missing.append("blood group")
-            if gender_model is None:
-                missing.append("gender")
-            missing_text = ", ".join(missing)
-            msg = f"Model files not deployed on Vercel. Missing: {missing_text}."
-            return msg, "", msg, ""
-
-        # Preprocess for both models
-        preprocessed_blood = preprocess_fingerprint(image, model_type="blood_group")
-        preprocessed_gender = preprocess_fingerprint(image, model_type="gender")
-        
-        # Get predictions
-        blood_result = predict_blood_group(blood_model, preprocessed_blood)
-        gender_result = predict_gender(gender_model, preprocessed_gender)
-        
-        # Format results
-        if blood_result["success"]:
-            blood_text = f"**Predicted Blood Group:** {blood_result['blood_group']}\n**Confidence:** {blood_result['confidence']:.2%}"
-            blood_details = "\n".join([f"{k}: {v:.2%}" for k, v in blood_result['all_scores'].items()])
-        else:
-            blood_text = "Error predicting blood group"
-            blood_details = str(blood_result.get('error', 'Unknown error'))
-        
-        if gender_result["success"]:
-            gender_text = f"**Predicted Gender:** {gender_result['gender']}\n**Confidence:** {gender_result['confidence']:.2%}"
-        else:
-            gender_text = "Error predicting gender"
-        
-        return blood_text, blood_details, gender_text, ""
-    
-    except Exception as e:
-        return f"Error processing image: {str(e)}", "", "", ""
-
-# Create Gradio interface
-with gr.Blocks(title="Forensic Fingerprint Analysis", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🔍 Forensic Fingerprint Analysis System")
-    gr.Markdown("Upload a fingerprint image to predict blood group and gender using machine learning models.")
-    
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(label="Upload Fingerprint Image", type="pil")
-            analyze_btn = gr.Button("Analyze Fingerprint", variant="primary")
-        
-        with gr.Column():
-            gr.Markdown("### Results")
-            blood_output = gr.Textbox(label="Blood Group Prediction", lines=2)
-            blood_scores = gr.Textbox(label="All Blood Group Scores", lines=8)
-            gender_output = gr.Textbox(label="Gender Prediction", lines=2)
-    
-    # Connect the function
-    analyze_btn.click(
-        analyze_fingerprint,
-        inputs=image_input,
-        outputs=[blood_output, blood_scores, gender_output, gr.Textbox(visible=False)]
-    )
-    
-    gr.Markdown("---")
-    gr.Markdown("**About:** 4th Year Forensic Research Project - Analyzing fingerprint patterns for biometric identification.")
-
-# Launch the app
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 7860))
-    demo.launch(server_name="0.0.0.0", server_port=port)
+import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+from PIL import Image
+import os
+
+# ===================== LOAD MODELS =====================
+
+blood_model = None
+gender_model = None
+
+def load_models():
+    global blood_model, gender_model
+
+    if blood_model is None:
+        try:
+            blood_model = tf.keras.models.load_model("models/blood_model.h5")
+        except Exception as e:
+            print(f"Blood model error: {e}")
+            blood_model = None
+
+    if gender_model is None:
+        try:
+            gender_model = tf.keras.models.load_model("models/gender_model.h5")
+        except Exception as e:
+            print(f"Gender model error: {e}")
+            gender_model = None
+
+    return blood_model, gender_model
+
+
+# ===================== PREPROCESS =====================
+
+def preprocess_image(image):
+    image = np.array(image)
+    img = cv2.resize(image, (224, 224))
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+
+# ===================== LABELS =====================
+
+BLOOD_GROUPS = ["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"]
+GENDERS = ["Male", "Female"]
+
+
+# ===================== PREDICTION =====================
+
+def analyze_fingerprint(image):
+
+    if image is None:
+        return "Please upload image", "", ""
+
+    try:
+        blood_model, gender_model = load_models()
+
+        if blood_model is None or gender_model is None:
+            return "Model files missing", "", ""
+
+        img = preprocess_image(image)
+
+        # Blood prediction
+        blood_pred = blood_model.predict(img)[0]
+        blood_idx = np.argmax(blood_pred)
+
+        blood_text = f"Predicted Blood Group: {BLOOD_GROUPS[blood_idx]} ({blood_pred[blood_idx]:.2%})"
+        blood_scores = "\n".join(
+            [f"{bg}: {score:.2%}" for bg, score in zip(BLOOD_GROUPS, blood_pred)]
+        )
+
+        # Gender prediction
+        gender_pred = gender_model.predict(img)[0]
+        gender_idx = np.argmax(gender_pred)
+
+        gender_text = f"Predicted Gender: {GENDERS[gender_idx]} ({gender_pred[gender_idx]:.2%})"
+
+        return blood_text, blood_scores, gender_text
+
+    except Exception as e:
+        return f"Error: {str(e)}", "", ""
+
+
+# ===================== UI =====================
+
+with gr.Blocks(title="Forensic Fingerprint Analysis") as demo:
+    gr.Markdown("# 🔍 Forensic Fingerprint Analysis")
+    gr.Markdown("Upload a fingerprint image to predict blood group and gender.")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Fingerprint")
+            btn = gr.Button("Analyze")
+
+        with gr.Column():
+            blood_output = gr.Textbox(label="Blood Group")
+            blood_scores = gr.Textbox(label="All Scores")
+            gender_output = gr.Textbox(label="Gender")
+
+    btn.click(
+        analyze_fingerprint,
+        inputs=image_input,
+        outputs=[blood_output, blood_scores, gender_output],
+    )
+
+
+# ===================== RUN =====================
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)