Update app.py

app.py

@@ -1,86 +1,54 @@
-import pandas as pd
 import gradio as gr
-import hashlib
-from datetime import datetime
+import torch
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import requests
 
-# Function to create a vCard entry with UID and REV fields
-def create_vcf(row):
-    # Generate a unique UID based on the email (using a hash for consistency)
-    email = row['Email']
-    uid = hashlib.md5(email.encode()).hexdigest()
-    rev = datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ')  # Current timestamp for revision
+# Load a pre-trained Vision Transformer model from Hugging Face
+model_name = "nateraw/vit-base-patch16-224-in21k"  # Replace with the model you've trained or a similar model
+model = ViTForImageClassification.from_pretrained(model_name)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
 
-    vcf_template = """BEGIN:VCARD
-VERSION:3.0
-PRODID:-//Apple Inc.//iPhone OS 17.2.1//EN
-N:{name}
-FN:{name}
-ORG:BAT Bangladesh;{function}
-TITLE:{designation}
-TEL;TYPE=CELL;TYPE=VOICE;TYPE=pref:{number}
-EMAIL:{email}
-UID:{uid}
-REV:{rev}
-END:VCARD"""
-    return vcf_template.format(
-        name=row['Name'],
-        function=row['Function'],
-        designation=row['Designation'],
-        number=row['Number'],
-        email=email,
-        uid=uid,
-        rev=rev
-    )
+# Define the disease labels (placeholders)
+labels = {
+    0: "Healthy",
+    1: "Tobacco Mosaic Virus",
+    2: "Brown Spot",
+    3: "Frog Eye Leaf Spot",
+    4: "Other"
+}
 
-# Function to generate the VCF file
-def generate_vcf(file):
-    # Load the Excel file
-    df = pd.read_excel(file)
-    df['Number'] = df['Number'].astype(str)  # Ensure phone numbers are strings
+# Define a function for disease detection
+def detect_disease(image):
+    # Preprocess the image
+    inputs = feature_extractor(images=image, return_tensors="pt")
     
-    # Generate the VCF data
-    vcf_data = df.apply(create_vcf, axis=1).str.cat(sep='\n\n')
+    # Run the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = logits.argmax().item()
     
-    # Write the VCF data to a file
-    vcf_file_name = '/tmp/BAT_New_Contacts.vcf'
-    with open(vcf_file_name, 'w') as vcf_file:
-        vcf_file.write(vcf_data)
+    # Get disease name from label dictionary
+    disease_name = labels.get(predicted_class, "Unknown Disease")
     
-    return vcf_file_name
-
-# Custom CSS and HTML
-css = """
-.gradio-container {
-    background: rgb(14, 43, 99);
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    color: #fff;
-    padding: 20px;
-}
-.gradio-input, .gradio-output {
-    background: rgb(28, 56, 113);
-    color: #fff;
-}
-footer {
-    display: none !important;
-}
-"""
+    return f"Disease Detected: {disease_name}"
 
-html_content = """
-<div style="text-align: center; margin-bottom: 20px;">
-    <img src="https://i.ibb.co/RbQRzcy/APMEA-CENTRAL-White.png" border="0" alt='BAT Bangladesh Logo' style='max-width: 300px;'>
-</div>
-<p style="text-align: center; color: #fff;">Upload an Excel file containing contact information in the following format: Name, Designation, Function, GRADE, Email, Number. The output will be a VCF file containing the contact information.</p>
+# Build Gradio Interface
+title = "Tobacco Leaf Disease Detection"
+description = """
+Upload or take a real-time picture of a tobacco leaf, and the app will detect the disease (if any).
 """
 
-# Gradio Interface
-with gr.Blocks(css=css) as demo:
-    gr.HTML(html_content)
-    gr.Interface(
-        fn=generate_vcf,
-        inputs=gr.File(label="Upload Excel File"),
-        outputs=gr.File(label="Download VCF File"),
-    )
+# Create Gradio interface with camera and real-time processing
+iface = gr.Interface(
+    fn=detect_disease,
+    inputs=gr.Image(source="camera", type="pil", tool="editor"),
+    outputs="text",
+    title=title,
+    description=description,
+    live=True  # Enables real-time processing
+)
 
-demo.launch()
+# Launch Gradio app
+iface.launch()