Row 6736

Row ID: 6736 | Dataset Entry | Axioma AXP Content Repository

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import streamlit as st import cv2 import numpy as np from tensorflow.keras.models import load_model import base64 from fpdf import FPDF # Load the trained model model = load_model("model.hdf5") # Dictionary mapping class numbers to class names class_names = {     0: "Plant: Apple\nDisease: Apple scab",     1: "Plant: Apple\nDisease: Black rot",     2: "Plant: Apple\nDisease: Cedar apple rust",     3: "Plant: Apple\nDisease: healthy",     4: "Plant: Blueberry\nDisease: healthy",     5: "Plant: Cherry (including sour)\nDisease: healthy",     6: "Plant: Cherry (including sour)\nDisease: Powdery mildew",     7: "Plant: Corn (maize)\nDisease: Cercospora leaf spot Gray leaf spot",     8: "Plant: Corn (maize)\nDisease: Common rust",     9: "Plant: Corn (maize)\nDisease: healthy",     10: "Plant: Corn (maize)\nDisease: Northern Leaf Blight",     11: "Plant: Grape\nDisease: Black rot",     12: "Plant: Grape\nDisease: Esca (Black Measles)",     13: "Plant: Grape\nDisease: healthy",     14: "Plant: Grape\nDisease: Leaf blight (Isariopsis Leaf Spot)",     15: "Plant: Orange\nDisease: Haunglongbing (Citrus greening)",     16: "Plant: Peach\nDisease: Bacterial spot",     17: "Plant: Peach\nDisease: healthy",     18: "Plant: Pepper\nDisease: bell Bacterial spot",     19: "Plant: Pepper\nDisease: bell healthy",     20: "Plant: Potato\nDisease: Early blight",     21: "Plant: Potato\nDisease: healthy",     22: "Plant: Potato\nDisease: Late blight",     23: "Plant: Raspberry\nDisease: healthy",     24: "Plant: Soybean\nDisease: healthy",     25: "Plant: Squash\nDisease: Powdery mildew",     26: "Plant: Strawberry\nDisease: healthy",     27: "Plant: Strawberry\nDisease: Leaf scorch",     28: "Plant: Tomato\nDisease: Bacterial spot",     29: "Plant: Tomato\nDisease: Early blight",     30: "Plant: Tomato\nDisease: healthy",     31: "Plant: Tomato\nDisease: Late blight",     32: "Plant: Tomato\nDisease: Leaf Mold",     33: "Plant: Tomato\nDisease: Septoria leaf spot",     34: "Plant: Tomato\nDisease: Spider mites Two-spotted spider mite",     35: "Plant: Tomato\nDisease: Target Spot",     36: "Plant: Tomato\nDisease: Tomato mosaic virus",     37: "Plant: Tomato\nDisease: Tomato Yellow Leaf Curl Virus" } # Function for preprocessing an uploaded image def preprocess_image(image):     img = cv2.resize(image, (224, 224))     img = img / 255.0  # Normalize pixel values     return img # Function to create a PDF report def create_pdf(image, predicted_class_name):     pdf = FPDF()     pdf.add_page()     pdf.set_font('Arial', 'B', 16)     pdf.cell(200, 10, txt="Plant Disease Detection Report", ln=True, align="C")     pdf.ln(10)     pdf.cell(200, 10, txt="Uploaded Image", ln=True, align="L")     pdf.ln(10)     # Convert the image to JPEG format and encode as base64     _, buffer = cv2.imencode('.jpg', image)     image_data = base64.b64encode(buffer).decode()     pdf.image('data:image/jpeg;base64,' + image_data, x=10, y=None, w=180)     pdf.ln(10)     pdf.cell(200, 10, txt="Prediction", ln=True, align="L")     pdf.ln(10)     pdf.multi_cell(0, 10, predicted_class_name)     return pdf # Streamlit app st.title("Plant Disease Detection") # File uploader widget uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) if uploaded_file is not None:     # Read the uploaded image     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)     uploaded_image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)         # Display the uploaded image     st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)         # Add a button to trigger prediction     if st.button('Predict'):         # Preprocess the image         processed_image = preprocess_image(uploaded_image)                 # Make prediction         prediction = model.predict(np.expand_dims(processed_image, axis=0))         predicted_class = np.argmax(prediction)                 # Get the class name corresponding to the predicted class number         predicted_class_name = class_names.get(predicted_class, "Unknown")                 # Display the prediction         st.write(f"Predicted Class: {predicted_class_name}")                 # Add button to generate PDF report         export_as_pdf = st.button("Export Report")         if export_as_pdf:             # Create PDF report             pdf_report = create_pdf(uploaded_image, predicted_class_name)                         # Generate a download link for the PDF             pdf_bytes = pdf_report.output(dest="S").encode("latin-1")             b64 = base64.b64encode(pdf_bytes).decode()             href = f'<a href="data:application/octet-stream;base64,{b64}" download="plant_disease_report.pdf">Download Report</a>'             st.markdown(href, unsafe_allow_html=True)

FieldValue
text import streamlit as st import cv2 import numpy as np from tensorflow.keras.models import load_model import base64 from fpdf import FPDF # Load the trained model model = load_model("model.hdf5") # Dictionary mapping class numbers to class names class_names = {     0: "Plant: Apple\nDisease: Apple scab",     1: "Plant: Apple\nDisease: Black rot",     2: "Plant: Apple\nDisease: Cedar apple rust",     3: "Plant: Apple\nDisease: healthy", …
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-13
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url_encoded Z0FBQUFBQm5Lak9Hb0VnYWtBNmpDSG55VFJVMk5sX25pdXVFOHBldEtOLWhfdEJTdXFMOVBlOFVxdjZySlpOdENmX0hwWmI4cURyV25wT0NuQXJPMGQtVzFDaVQ3UVFzRUx4WkFkVEtaMENVVnlTZTluRGZIbVB6T044amhLa3RNUHBIZVRJYXg1bmdzWDJ2ZjkzYWNjY0o4eDFDbE9EOTVCTExEMnROSnExM3ZpeUctc0xqYTBNNjFSODJ0ZDRDSmhVdTRZWmFfNXZz

Raw Record

{
  "text": "    import streamlit as st\n    import cv2\n    import numpy as np\n    from tensorflow.keras.models import load_model\n    import base64\n    from fpdf import FPDF\n    \n    # Load the trained model\n    model = load_model(\"model.hdf5\")\n    \n    # Dictionary mapping class numbers to class names\n    class_names = {\n        0: \"Plant: Apple\\nDisease: Apple scab\",\n        1: \"Plant: Apple\\nDisease: Black rot\",\n        2: \"Plant: Apple\\nDisease: Cedar apple rust\",\n        3: \"Plant: Apple\\nDisease: healthy\",\n        4: \"Plant: Blueberry\\nDisease: healthy\",\n        5: \"Plant: Cherry (including sour)\\nDisease: healthy\",\n        6: \"Plant: Cherry (including sour)\\nDisease: Powdery mildew\",\n        7: \"Plant: Corn (maize)\\nDisease: Cercospora leaf spot Gray leaf spot\",\n        8: \"Plant: Corn (maize)\\nDisease: Common rust\",\n        9: \"Plant: Corn (maize)\\nDisease: healthy\",\n        10: \"Plant: Corn (maize)\\nDisease: Northern Leaf Blight\",\n        11: \"Plant: Grape\\nDisease: Black rot\",\n        12: \"Plant: Grape\\nDisease: Esca (Black Measles)\",\n        13: \"Plant: Grape\\nDisease: healthy\",\n        14: \"Plant: Grape\\nDisease: Leaf blight (Isariopsis Leaf Spot)\",\n        15: \"Plant: Orange\\nDisease: Haunglongbing (Citrus greening)\",\n        16: \"Plant: Peach\\nDisease: Bacterial spot\",\n        17: \"Plant: Peach\\nDisease: healthy\",\n        18: \"Plant: Pepper\\nDisease: bell Bacterial spot\",\n        19: \"Plant: Pepper\\nDisease: bell healthy\",\n        20: \"Plant: Potato\\nDisease: Early blight\",\n        21: \"Plant: Potato\\nDisease: healthy\",\n        22: \"Plant: Potato\\nDisease: Late blight\",\n        23: \"Plant: Raspberry\\nDisease: healthy\",\n        24: \"Plant: Soybean\\nDisease: healthy\",\n        25: \"Plant: Squash\\nDisease: Powdery mildew\",\n        26: \"Plant: Strawberry\\nDisease: healthy\",\n        27: \"Plant: Strawberry\\nDisease: Leaf scorch\",\n        28: \"Plant: Tomato\\nDisease: Bacterial spot\",\n        29: \"Plant: Tomato\\nDisease: Early blight\",\n        30: \"Plant: Tomato\\nDisease: healthy\",\n        31: \"Plant: Tomato\\nDisease: Late blight\",\n        32: \"Plant: Tomato\\nDisease: Leaf Mold\",\n        33: \"Plant: Tomato\\nDisease: Septoria leaf spot\",\n        34: \"Plant: Tomato\\nDisease: Spider mites Two-spotted spider mite\",\n        35: \"Plant: Tomato\\nDisease: Target Spot\",\n        36: \"Plant: Tomato\\nDisease: Tomato mosaic virus\",\n        37: \"Plant: Tomato\\nDisease: Tomato Yellow Leaf Curl Virus\"\n    }\n    \n    # Function for preprocessing an uploaded image\n    def preprocess_image(image):\n        img = cv2.resize(image, (224, 224))\n        img = img / 255.0  # Normalize pixel values\n        return img\n    \n    # Function to create a PDF report\n    def create_pdf(image, predicted_class_name):\n        pdf = FPDF()\n        pdf.add_page()\n        pdf.set_font('Arial', 'B', 16)\n        pdf.cell(200, 10, txt=\"Plant Disease Detection Report\", ln=True, align=\"C\")\n        pdf.ln(10)\n        pdf.cell(200, 10, txt=\"Uploaded Image\", ln=True, align=\"L\")\n        pdf.ln(10)\n        # Convert the image to JPEG format and encode as base64\n        _, buffer = cv2.imencode('.jpg', image)\n        image_data = base64.b64encode(buffer).decode()\n        pdf.image('data:image/jpeg;base64,' + image_data, x=10, y=None, w=180)\n        pdf.ln(10)\n        pdf.cell(200, 10, txt=\"Prediction\", ln=True, align=\"L\")\n        pdf.ln(10)\n        pdf.multi_cell(0, 10, predicted_class_name)\n        return pdf\n    \n    # Streamlit app\n    st.title(\"Plant Disease Detection\")\n    \n    # File uploader widget\n    uploaded_file = st.file_uploader(\"Upload an image\", type=[\"jpg\", \"jpeg\", \"png\"])\n    \n    if uploaded_file is not None:\n        # Read the uploaded image\n        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)\n        uploaded_image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)\n        \n        # Display the uploaded image\n        st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)\n        \n        # Add a button to trigger prediction\n        if st.button('Predict'):\n            # Preprocess the image\n            processed_image = preprocess_image(uploaded_image)\n            \n            # Make prediction\n            prediction = model.predict(np.expand_dims(processed_image, axis=0))\n            predicted_class = np.argmax(prediction)\n            \n            # Get the class name corresponding to the predicted class number\n            predicted_class_name = class_names.get(predicted_class, \"Unknown\")\n            \n            # Display the prediction\n            st.write(f\"Predicted Class: {predicted_class_name}\")\n            \n            # Add button to generate PDF report\n            export_as_pdf = st.button(\"Export Report\")\n            if export_as_pdf:\n                # Create PDF report\n                pdf_report = create_pdf(uploaded_image, predicted_class_name)\n                \n                # Generate a download link for the PDF\n                pdf_bytes = pdf_report.output(dest=\"S\").encode(\"latin-1\")\n                b64 = base64.b64encode(pdf_bytes).decode()\n                href = f'<a href=\"data:application/octet-stream;base64,{b64}\" download=\"plant_disease_report.pdf\">Download Report</a>'\n                st.markdown(href, unsafe_allow_html=True)\n    \n    ",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-13",
  "username_encoded": "Z0FBQUFBQm5LakwzeHdDZXRabDk0QTJWX0V2Q2MwTnZublFXNmpxUWNwdXBLeHlfTWhQNjlrTFFMSEhpaFljSUJNVXYtejVhdDZpWXdhQ3phaUt5SUdUOUtmQVE5YlJ6VU1lbFFVUFF0VWNyOWJHbW9qd0diYTA9",
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}
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