initial

README.md

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----
-title: Blooddetection
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# blood-group-disease-identification

Thalassemia.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f7dbccb3d7e70b0855857c104aebe7651046fca6548979a0b55f1412cf1e0d
+size 2965121

app.py

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+from PIL import Image
+import numpy as np
+import torch
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image as keras_image
+from ultralytics import YOLO
+import gradio as gr
+
+# === Load Models ===
+clot_model = load_model("blood_group/clot_classifier_model.h5")
+sickle_model = load_model("sickle_cell_model_mobnetv2.h5")
+thalassemia_model = YOLO("Thalassemia.pt")  # YOLOv8 classifier
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+leukemia_model = torch.hub.load("ultralytics/yolov5", "custom", path="bestluk.pt", force_reload=True)
+
+# Restore PosixPath
+pathlib.PosixPath = temp
+
+# === Clot Detection ===
+def is_clotted(img):
+    img = img.convert("RGB").resize((128, 128))
+    arr = keras_image.img_to_array(img)
+    arr = arr.astype("float32") / 255.0
+    arr = np.expand_dims(arr, axis=0)
+    pred = clot_model.predict(arr)[0][0]
+    return pred < 0.5  # True = clotted
+
+def predict_clot(image):
+    try:
+        if image is None:
+            return "❌ No image uploaded."
+        label = "Clotted" if is_clotted(image) else "Non-Clotted"
+        return f"🩸 Clot Detection: *{label}*"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# === Sickle Cell Detection ===
+def predict_sickle(image):
+    try:
+        if image is None:
+            return "❌ No image uploaded."
+        img = image.convert("RGB").resize((128, 128))
+        arr = keras_image.img_to_array(img)
+        arr = arr.astype("float32") / 255.0
+        arr = np.expand_dims(arr, axis=0)
+        pred = sickle_model.predict(arr)[0][0]
+        label = "Normal" if pred >= 0.5 else "Sickle Cell"
+        confidence = pred if pred >= 0.5 else 1 - pred
+        return f"🧬 Sickle Cell Detection: *{label}*\nConfidence: {confidence:.2f}"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# === Blood Group Identification ===
+def split_into_three_drops(image):
+    width, height = image.size
+    third = width // 3
+    return (
+        image.crop((0, 0, third, height)),
+        image.crop((third, 0, 2 * third, height)),
+        image.crop((2 * third, 0, width, height))
+    )
+
+def determine_blood_group(a_clot, b_clot, d_clot):
+    if a_clot and b_clot:
+        base = "AB"
+    elif a_clot:
+        base = "A"
+    elif b_clot:
+        base = "B"
+    else:
+        base = "O"
+    rh = "+" if d_clot else "-"
+    return base + rh
+
+def detect_blood_group(image):
+    try:
+        drop_a, drop_b, drop_d = split_into_three_drops(image)
+        a_clot = is_clotted(drop_a)
+        b_clot = is_clotted(drop_b)
+        d_clot = is_clotted(drop_d)
+        group = determine_blood_group(a_clot, b_clot, d_clot)
+        return (
+            drop_a, drop_b, drop_d,
+            f"🧪 Blood Group: *{group}*\n\nClotting:\n- Anti-A: {'Yes' if a_clot else 'No'}\n- Anti-B: {'Yes' if b_clot else 'No'}\n- Anti-D: {'Yes' if d_clot else 'No'}"
+        )
+    except Exception as e:
+        return None, None, None, f"❌ Error: {str(e)}"
+
+# === Thalassemia Classification (YOLOv8) ===
+def predict_thalassemia(image):
+    try:
+        results = thalassemia_model(image, save=False)[0]
+        top_class_idx = int(results.probs.top1)
+        top_class_label = thalassemia_model.names[top_class_idx]
+        confidence = results.probs.data[top_class_idx]
+        return f"🧬 Thalassemia Classification: *{top_class_label}*\nConfidence: {confidence:.2f}"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# === Leukemia Detection (YOLOv5) ===
+class_info = {
+    'Benign': "Stage: Benign cells. No immediate concern, monitor routinely.",
+    'Early': "Stage: Early leukemia detected. Please consult a hematologist.",
+    'Pre': "Stage: Pre-leukemic condition. Immediate medical consultation recommended.",
+    'Pro': "Stage: Pro-leukemic (progressed). Urgent specialist intervention required."
+}
+
+def predict_leukemia(image):
+    try:
+        image_path = "temp_leukemia.jpg"
+        image.save(image_path)
+
+        results = leukemia_model(image_path)
+        predictions = results.pandas().xyxy[0]
+
+        if predictions.empty:
+            return "🧬 No abnormal cells detected. Please check image quality."
+
+        labels = predictions['name'].value_counts().to_dict()
+        report = "🧬 *Leukemia Detection Summary*:\n"
+        for cls, count in labels.items():
+            report += f"\n- *{cls}*: {count} cell(s)\n  → {class_info.get(cls, 'No info available.')}"
+        return report
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# === Gradio GUI ===
+with gr.Blocks(css="body { background-color: white; color: #111; }") as demo:
+    gr.Markdown("## 🩸 Blood Group & Disease Detection System")
+    gr.Markdown("Select a task, upload an image, and click Predict.")
+
+    task_selector = gr.Dropdown(
+        choices=[
+            "Check Clotness",
+            "Blood Group Identification",
+            "Sickle Cell Detection",
+            "Thalassemia Detection",
+            "Leukemia Detection"
+        ],
+        label="Select Task",
+        value=None,
+        interactive=True
+    )
+
+    # Clotness Section
+    with gr.Column(visible=False) as clot_section:
+        clot_image = gr.Image(type="pil", label="Upload Blood Drop Image")
+        clot_result = gr.Textbox(label="Prediction Result", lines=4)
+        with gr.Row():
+            clot_predict = gr.Button("🔍 Predict")
+            clot_clear = gr.Button("🔄 Clear")
+        clot_predict.click(fn=predict_clot, inputs=clot_image, outputs=clot_result)
+        clot_clear.click(fn=lambda: (None, ""), outputs=[clot_image, clot_result])
+
+    # Blood Group Section
+    with gr.Column(visible=False) as group_section:
+        group_image = gr.Image(type="pil", label="Upload 3-Drop Blood Image")
+        with gr.Row():
+            drop_a_img = gr.Image(label="Anti-A Drop", interactive=False)
+            drop_b_img = gr.Image(label="Anti-B Drop", interactive=False)
+            drop_d_img = gr.Image(label="Anti-D Drop", interactive=False)
+        group_result = gr.Textbox(label="Prediction Result", lines=6)
+        with gr.Row():
+            group_predict = gr.Button("🔍 Predict Blood Group")
+            group_clear = gr.Button("🔄 Clear")
+        group_predict.click(fn=detect_blood_group, inputs=group_image, outputs=[drop_a_img, drop_b_img, drop_d_img, group_result])
+        group_clear.click(fn=lambda: (None, None, None, None, ""), outputs=[group_image, drop_a_img, drop_b_img, drop_d_img, group_result])
+
+    # Sickle Cell Section
+    with gr.Column(visible=False) as sickle_section:
+        sickle_image = gr.Image(type="pil", label="Upload Blood Cell Image")
+        sickle_result = gr.Textbox(label="Prediction Result", lines=4)
+        with gr.Row():
+            sickle_predict = gr.Button("🔍 Predict")
+            sickle_clear = gr.Button("🔄 Clear")
+        sickle_predict.click(fn=predict_sickle, inputs=sickle_image, outputs=sickle_result)
+        sickle_clear.click(fn=lambda: (None, ""), outputs=[sickle_image, sickle_result])
+
+    # Thalassemia Section
+    with gr.Column(visible=False) as thal_section:
+        thal_image = gr.Image(type="pil", label="Upload Blood Image")
+        thal_result = gr.Textbox(label="Prediction Result", lines=4)
+        with gr.Row():
+            thal_predict = gr.Button("🔍 Predict")
+            thal_clear = gr.Button("🔄 Clear")
+        thal_predict.click(fn=predict_thalassemia, inputs=thal_image, outputs=thal_result)
+        thal_clear.click(fn=lambda: (None, ""), outputs=[thal_image, thal_result])
+
+    # Leukemia Section
+    with gr.Column(visible=False) as leukemia_section:
+        leukemia_image = gr.Image(type="pil", label="Upload Microscopic Blood Image")
+        leukemia_result = gr.Textbox(label="Prediction Result", lines=8)
+        with gr.Row():
+            leukemia_predict = gr.Button("🔍 Predict")
+            leukemia_clear = gr.Button("🔄 Clear")
+        leukemia_predict.click(fn=predict_leukemia, inputs=leukemia_image, outputs=leukemia_result)
+        leukemia_clear.click(fn=lambda: (None, ""), outputs=[leukemia_image, leukemia_result])
+
+    # Toggle UI Sections
+    def show_section(task):
+        return (
+            gr.update(visible=task == "Check Clotness"),
+            gr.update(visible=task == "Blood Group Identification"),
+            gr.update(visible=task == "Sickle Cell Detection"),
+            gr.update(visible=task == "Thalassemia Detection"),
+            gr.update(visible=task == "Leukemia Detection")
+        )
+
+    task_selector.change(fn=show_section, inputs=task_selector, outputs=[
+        clot_section, group_section, sickle_section, thal_section, leukemia_section
+    ])
+
+demo.launch(share=True)

bestluk.pt

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+oid sha256:5fceee94be2967a69b7a44373bf77a9bbfa9cc3fd6f81deb41f4546e597cb239
+size 14458152

blood_group/clot_classifier_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a08b11f45c455d272d84d9264d3d862f042049cd6ba68bb2568c53119a721ef6
+size 39704352

clot_classifier_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a08b11f45c455d272d84d9264d3d862f042049cd6ba68bb2568c53119a721ef6
+size 39704352

packages.txt

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+libgl1
+ffmpeg
+python3-distutils

requirements.txt

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+streamlit==1.35.0
+numpy>=1.23,<1.26
+Pillow==9.5.0
+tensorflow==2.12.0
+torch==2.0.1
+ultralytics==8.0.174

sickle_cell_model_mobnetv2.h5

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+oid sha256:9153b7820cf59dfccdf9993581ff649a60a55c320fc5e5a12c51432e0cfb4d72
+size 28056472