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yassonee commited on Jan 27

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2c3deb2

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1 Parent(s): 5879dd0

Update app.py

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app.py +81 -286

app.py CHANGED Viewed

@@ -1,301 +1,96 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import HTMLResponse
-from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
-from PIL import Image
-import io
-import uvicorn
-import base64
-app = FastAPI()
 # Chargement des modèles
-def load_models():
-    return {
-        "chest_classifier": pipeline("image-classification", model="codewithdark/vit-chest-xray")
-    }
-models = load_models()
-def image_to_base64(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode()
-    return f"data:image/png;base64,{img_str}"
-COMMON_STYLES = """
-    body {
-        font-family: system-ui, -apple-system, sans-serif;
-        background: #f0f2f5;
-        margin: 0;
-        padding: 20px;
-        color: #1a1a1a;
-    }
-    ::-webkit-scrollbar {
-        width: 8px;
-        height: 8px;
-    }
-    ::-webkit-scrollbar-track {
-        background: transparent;
-    }
-    ::-webkit-scrollbar-thumb {
-        background-color: rgba(156, 163, 175, 0.5);
-        border-radius: 4px;
-    }
-    .container {
-        max-width: 1200px;
-        margin: 0 auto;
-        background: white;
-        padding: 20px;
-        border-radius: 10px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-    .button {
-        background: #2d2d2d;
-        color: white;
-        border: none;
-        padding: 12px 30px;
-        border-radius: 8px;
-        cursor: pointer;
-        font-size: 1.1em;
-        transition: all 0.3s ease;
-        position: relative;
-    }
-    .button:hover {
-        background: #404040;
-    }
-    @keyframes progress {
-        0% { width: 0; }
-        100% { width: 100%; }
-    }
-    @keyframes blink {
-        0% { opacity: 1; }
-        50% { opacity: 0; }
-        100% { opacity: 1; }
-    }
-    #loading {
-        display: none;
-        color: white;
-        margin-top: 10px;
-        animation: blink 1s infinite;
-        text-align: center;
-    }
-    .button-progress {
-        position: absolute;
-        bottom: 0;
-        left: 0;
-        height: 4px;
-        background: rgba(255, 255, 255, 0.5);
-        width: 0;
-    }
-    .button:active .button-progress {
-        animation: progress 2s linear forwards;
-    }
-    img {
-        max-width: 100%;
-        height: auto;
-        border-radius: 8px;
-    }
-"""
-@app.get("/", response_class=HTMLResponse)
-async def main():
-    content = f"""
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>Chest X-Ray Analysis</title>
-        <meta name="viewport" content="width=device-width, initial-scale=1.0">
-        <style>
-            {COMMON_STYLES}
-            .upload-section {{
-                background: #2d2d2d;
-                padding: 40px;
-                border-radius: 12px;
-                margin: 20px 0;
-                text-align: center;
-                border: 2px dashed #404040;
-                transition: all 0.3s ease;
-                color: white;
-            }}
-            .upload-section:hover {{
-                border-color: #555;
-            }}
-            input[type="file"] {{
-                font-size: 1.1em;
-                margin: 20px 0;
-                color: white;
-            }}
-            input[type="file"]::file-selector-button {{
-                font-size: 1em;
-                padding: 10px 20px;
-                border-radius: 8px;
-                border: 1px solid #404040;
-                background: #2d2d2d;
-                color: white;
-                transition: all 0.3s ease;
-                cursor: pointer;
-            }}
-            input[type="file"]::file-selector-button:hover {{
-                background: #404040;
-            }}
-            .preview-image {{
-                max-width: 300px;
-                margin: 20px auto;
-                display: none;
-            }}
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <div class="upload-section">
-                <form action="/analyze" method="post" enctype="multipart/form-data" onsubmit="document.getElementById('loading').style.display = 'block';">
-                    <div>
-                        <input type="file" name="file" accept="image/*" required
-                               onchange="document.getElementById('preview').src = window.URL.createObjectURL(this.files[0]);
-                                        document.getElementById('preview').style.display = 'block';">
-                    </div>
-                    <img id="preview" class="preview-image" src="" alt="Preview">
-                    <button type="submit" class="button">
-                        Analyze X-Ray
-                        <div class="button-progress"></div>
-                    </button>
-                    <div id="loading">Loading...</div>
-                </form>
-            </div>
-        </div>
-    </body>
-    </html>
-    """
-    return content
-@app.post("/analyze", response_class=HTMLResponse)
-async def analyze_file(file: UploadFile = File(...)):
     try:
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents))
-        # Get predictions from the model
-        predictions = models["chest_classifier"](image)
-        result_image_b64 = image_to_base64(image)
-        results_html = f"""
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <title>Results</title>
-            <meta name="viewport" content="width=device-width, initial-scale=1.0">
-            <style>
-                {COMMON_STYLES}
-                .results-grid {{
-                    display: grid;
-                    grid-template-columns: 1fr 1fr;
-                    gap: 20px;
-                    margin-top: 20px;
-                }}
-                .result-box {{
-                    background: white;
-                    padding: 20px;
-                    border-radius: 12px;
-                    margin: 10px 0;
-                    border: 1px solid #e9ecef;
-                }}
-                .score-high {{
-                    color: #0066cc;
-                    font-weight: bold;
-                }}
-                .score-medium {{
-                    color: #ffa500;
-                    font-weight: bold;
-                }}
-                .back-button {{
-                    display: inline-block;
-                    text-decoration: none;
-                    margin-top: 20px;
-                }}
-                h3 {{
-                    color: #0066cc;
-                    margin-top: 0;
-                }}
-                @media (max-width: 768px) {{
-                    .results-grid {{
-                        grid-template-columns: 1fr;
-                    }}
-                }}
-            </style>
-        </head>
-        <body>
-            <div class="container">
-                <div class="results-grid">
-                    <div class="result-box">
-                        <h3>Analysis Results</h3>
-        """
-        for pred in predictions:
-            confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
-            results_html += f"""
-                <div>
-                    <span class="{confidence_class}">{pred['score']:.1%}</span> -
-                    {pred['label']}
-                </div>
-            """
-        results_html += f"""
-                    </div>
-                    <div class='result-box'>
-                        <h3>X-Ray Image</h3>
-                        <img src="{result_image_b64}" alt="Analyzed X-Ray">
-                    </div>
-                </div>
-                <a href="/" class="button back-button">
-                    ← Back
-                    <div class="button-progress"></div>
-                </a>
-            </div>
-        </body>
-        </html>
-        """
-        return results_html
     except Exception as e:
-        return f"""
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <title>Error</title>
-            <meta name="viewport" content="width=device-width, initial-scale=1.0">
-            <style>
-                {COMMON_STYLES}
-                .error-box {{
-                    background: #fee2e2;
-                    border: 1px solid #ef4444;
-                    padding: 20px;
-                    border-radius: 8px;
-                    margin: 20px 0;
-                }}
-            </style>
-        </head>
-        <body>
-            <div class="container">
-                <div class="error-box">
-                    <h3>Error</h3>
-                    <p>{str(e)}</p>
-                </div>
-                <a href="/" class="button back-button">
-                    ← Back
-                    <div class="button-progress"></div>
-                </a>
-            </div>
-        </body>
-        </html>
-        """
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)

+import gradio as gr
+from transformers import pipeline
+import torch
+from PIL import Image, ImageDraw
+import numpy as np
 # Chargement des modèles
+classifier = pipeline("image-classification", model="abhishek/chest-xray-classification")
+detector = pipeline("object-detection", model="nickysam/detect-thorax-anomaly-75acc")
+def draw_boxes(image, predictions):
+    # Convertir l'image numpy en PIL si nécessaire
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(np.uint8(image))
+    draw = ImageDraw.Draw(image)
+    # Dessiner les boîtes de détection
+    for pred in predictions:
+        box = pred['box']
+        score = pred['score']
+        label = pred['label']
+        # Coordonnées de la boîte
+        x1, y1 = box['xmin'], box['ymin']
+        x2, y2 = box['xmax'], box['ymax']
+        # Couleur en fonction du score
+        color = (255, 0, 0) if score > 0.7 else (255, 165, 0)
+        # Dessiner le rectangle
+        draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
+        # Ajouter le label et le score
+        label_text = f"{label}: {score:.1%}"
+        draw.text((x1, y1-15), label_text, fill=color)
+    return image
+def analyze_xray(image):
     try:
+        # Classification générale
+        classifications = classifier(image)
+        # Détection des anomalies
+        detections = detector(image)
+        # Dessiner les boîtes sur l'image
+        annotated_image = draw_boxes(image, detections)
+        # Préparer les résultats
+        results = "Classifications:\n"
+        for pred in classifications:
+            results += f"{pred['label']}: {pred['score']:.1%}\n"
+        results += "\nDetected Anomalies:\n"
+        for det in detections:
+            results += f"{det['label']}: {det['score']:.1%}\n"
+        return annotated_image, results
     except Exception as e:
+        return image, f"Error: {str(e)}"
+# Interface Gradio
+with gr.Blocks(theme=gr.themes.Soft(
+    primary_hue="gray",
+    secondary_hue="gray",
+)) as demo:
+    gr.Markdown("""
+    # Chest X-Ray Analysis
+    This application analyzes chest X-rays to:
+    1. Classify general conditions
+    2. Detect and locate specific anomalies
+    """)
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Upload X-Ray Image", type="pil")
+            analyze_btn = gr.Button("Analyze", variant="primary")
+        with gr.Column():
+            output_image = gr.Image(label="Analyzed Image")
+            output_text = gr.Textbox(label="Results", lines=10)
+    analyze_btn.click(
+        fn=analyze_xray,
+        inputs=[input_image],
+        outputs=[output_image, output_text]
+    )
+    gr.Markdown("""
+    Note: This tool is for demonstration purposes only and should not be used for medical diagnosis.
+    """)
+# Lancement de l'application
+demo.launch()