Upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+alert-1.mp3 filter=lfs diff=lfs merge=lfs -text

alert-1.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bcc5a64db2908be4676e69a58a773925b3b36b9f7ea841fced19a8e4e4d05baf
+size 911040

app.py

@@ -1,46 +1,667 @@
 from ultralytics import YOLO
 import gradio as gr
+import numpy as np
 from PIL import Image
 
-# Load YOLOv5 model
-model = YOLO("best.pt")  # make sure best.pt is in the same directory
+# Load YOLO model
+model = YOLO("best.pt")
 
-# You can define a simple disease severity mapping like this:
-disease_severity = {
-    "Late_blight": "High",
-    "Early_blight": "Medium",
-    "Powdery_mildew": "Low",
-    "Healthy": "None",
-    # Add your disease names and severity levels here accordingly
-}
+# Define what class names indicate a human
+HUMAN_CLASSES = ["person", "human"]
 
-# Prediction function
+# Predict Function
 def predict(image):
-    results = model(image)
-    boxes = results[0].boxes
-    names = model.names
-    output = ""
-
-    if len(boxes) == 0:
-        output = "✅ Healthy: No disease detected!"
-    else:
-        for box in boxes:
-            class_id = int(box.cls[0])
-            disease_name = names[class_id]
-            confidence = float(box.conf[0])
-            
-            severity = disease_severity.get(disease_name, "Unknown")
-
-            output += f"⚠️ Disease: {disease_name}\n"
-            output += f"❗ Seriousness Level: {severity}\n"
-            output += f"🔍 Confidence: {confidence * 100:.2f}%\n\n"
-
-    return output.strip()
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="🌿 AgroScan: Plant Disease Detector",
-    description="Upload a plant leaf image to detect disease and get disease seriousness and confidence.",
-).launch()
+    image_status = "✅ Real Image"
+    disease_status = "✅ No Disease Detected"
+    details = ""
+    audio_html = "alert-1.mp3"  # for autoplay sound
+
+    try:
+        results = model(image)
+        boxes = results[0].boxes
+        class_names = model.names
+
+        found_human = False
+        found_disease = False
+
+        if len(boxes) > 0:
+            for box in boxes:
+                class_id = int(box.cls[0])
+                class_name = class_names[class_id].lower()
+                confidence = float(box.conf[0])
+                details += f"🔎 {class_name} - {confidence*100:.2f}%\n"
+
+                if class_name in HUMAN_CLASSES:
+                    found_human = True
+                else:
+                    found_disease = True
+
+        if found_human:
+            disease_status = "🚨 Human Detected (Invalid Input)"
+            audio_html = """<audio autoplay><source src="file/disease_alert.mp3" type="audio/mpeg"></audio>"""
+        elif found_disease:
+            disease_status = "🚨 Disease Detected"
+            audio_html = """<audio autoplay><source src="file/disease_alert.mp3" type="audio/mpeg"></audio>"""
+        else:
+            disease_status = "✅ No Disease Detected"
+
+    except Exception as e:
+        image_status = "❌ Error"
+        disease_status = "⚠️ Detection Failed"
+        details = str(e)
+
+    return image_status, disease_status, details, audio_html
+
+
+# Gradio Interface
+with gr.Blocks(title="🚨 AgroScan - Human Alert + Disease Detection") as demo:
+    gr.Markdown("""
+    <h1 style="text-align:center; color:#2e7d32;">🌿 AgroScan</h1>
+    <p style="text-align:center;">
+        Upload an image of a plant leaf. If a human or disease is detected, an alert will be triggered with sound 🚨.
+    </p>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="📷 Upload Image")
+            submit_btn = gr.Button("🔍 Analyze")
+        with gr.Column(scale=1):
+            result1 = gr.Textbox(label="🖼️ Image Status")
+            result2 = gr.Textbox(label="🌱 Disease Status")
+            result3 = gr.Textbox(label="📋 Detection Details", lines=5)
+            audio_out = gr.HTML(label="🚨 Auto Alert") # autoplay sound (no controls)
+
+    submit_btn.click(fn=predict, inputs=image_input,
+                     outputs=[result1, result2, result3, audio_out])
+
+demo.launch()
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# import numpy as np
+# from PIL import Image
+
+# # Load YOLO model
+# model = YOLO("best.pt")
+
+# # Define human classes
+# HUMAN_CLASSES = ["person", "human"]
+
+# # Prediction function
+# def predict(image):
+#     image_status = "✅ Real Image"
+#     disease_status = "✅ No Disease Detected"
+#     details = ""
+#     alert_sound = "alert-1.mp3"  # Will hold sound file path
+
+#     try:
+#         results = model(image)
+#         boxes = results[0].boxes
+#         class_names = model.names
+
+#         found_human = False
+#         found_disease = False
+
+#         if len(boxes) > 0:
+#             for box in boxes:
+#                 class_id = int(box.cls[0])
+#                 class_name = class_names[class_id].lower()
+#                 confidence = float(box.conf[0])
+#                 details += f"🔎 {class_name} - {confidence*100:.2f}%\n"
+
+#                 if class_name in HUMAN_CLASSES:
+#                     found_human = True
+#                 else:
+#                     found_disease = True
+
+#         if found_human:
+#             disease_status = "🚨 Human Detected (Invalid Input)"
+#             alert_sound = "disease_alert.mp3"
+#         elif found_disease:
+#             disease_status = "🚨 Disease Detected"
+#             alert_sound = "disease_alert.mp3"
+#         else:
+#             disease_status = "✅ No Disease Detected"
+
+#     except Exception as e:
+#         image_status = "❌ Error"
+#         disease_status = "⚠️ Detection Failed"
+#         details = str(e)
+
+#     return image_status, disease_status, details, alert_sound
+
+# # Gradio Interface
+# with gr.Blocks(title="🚨 AgroScan - Human Alert + Disease Detection") as demo:
+#     gr.Markdown("""
+#     <h1 style="text-align:center; color:#2e7d32;">🌿 AgroScan</h1>
+#     <p style="text-align:center;">
+#         Upload an image of a plant leaf. If a human or disease is detected, an alert will be triggered with sound 🚨.
+#     </p>
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column(scale=1):
+#             result1 = gr.Textbox(label="🖼️ Image Status")
+#             result2 = gr.Textbox(label="🌱 Disease Status")
+#             result3 = gr.Textbox(label="📋 Detection Details", lines=5)
+#             audio_out = gr.Audio(label="🚨 Alert Sound", autoplay=True)
+
+#     submit_btn.click(fn=predict, inputs=image_input,
+#                      outputs=[result1, result2, result3, audio_out])
+
+# demo.launch()
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# import numpy as np
+# from PIL import Image
+
+# # Load YOLO model
+# model = YOLO("best.pt")
+
+# # Define what class names indicate a human
+# HUMAN_CLASSES = ["person", "human"]
+
+# # Predict Function
+# def predict(image):
+#     image_status = "✅ Real Image"
+#     disease_status = "✅ No Disease Detected"
+#     details = ""
+#     alert_sound = None  # path to MP3 file
+
+#     try:
+#         results = model(image)
+#         boxes = results[0].boxes
+#         class_names = model.names
+
+#         found_human = False
+#         found_disease = False
+
+#         if len(boxes) > 0:
+#             for box in boxes:
+#                 class_id = int(box.cls[0])
+#                 class_name = class_names[class_id].lower()
+#                 confidence = float(box.conf[0])
+#                 details += f"🔎 {class_name} - {confidence*100:.2f}%\n"
+
+#                 if class_name in HUMAN_CLASSES:
+#                     found_human = True
+#                 else:
+#                     found_disease = True
+
+#         if found_human:
+#             disease_status = "🚨 Human Detected (Invalid Input)"
+#             alert_sound = "disease_alert.mp3"
+#         elif found_disease:
+#             disease_status = "🚨 Disease Detected"
+#             alert_sound = "disease_alert.mp3"
+#         else:
+#             disease_status = "✅ No Disease Detected"
+
+#     except Exception as e:
+#         image_status = "❌ Error"
+#         disease_status = "⚠️ Detection Failed"
+#         details = str(e)
+
+#     return image_status, disease_status, details, alert_sound
+
+# # Gradio Interface
+# with gr.Blocks(title="🚨 AgroScan - Human Alert + Disease Detection") as demo:
+#     gr.Markdown("""
+#     <h1 style="text-align:center; color:#2e7d32;">🌿 AgroScan</h1>
+#     <p style="text-align:center;">
+#         Upload an image of a plant leaf. If a human or disease is detected, an alert will be triggered with sound 🚨.
+#     </p>
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column(scale=1):
+#             result1 = gr.Textbox(label="🖼️ Image Status")
+#             result2 = gr.Textbox(label="🌱 Disease Status")
+#             result3 = gr.Textbox(label="📋 Detection Details", lines=5)
+#             audio_out = gr.Audio(label="🚨 Alert Sound", autoplay=True)
+
+#     submit_btn.click(fn=predict, inputs=image_input,
+#                      outputs=[result1, result2, result3, audio_out])
+
+# demo.launch()
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# import numpy as np
+# from PIL import Image
+
+# # Load the trained YOLOv8 model
+# model = YOLO("best.pt")
+
+# # Class name that represents "human" in your YOLO model
+# HUMAN_CLASS_NAMES = ["person", "human"]
+
+# # Prediction function with sound logic
+# def predict(image):
+#     image_status = "✅ Real Image"
+#     disease_status = "✅ No Disease Detected"
+#     disease_details = ""
+#     play_alert = None  # MP3 to be played
+
+#     try:
+#         results = model(image)
+#         boxes = results[0].boxes
+#         names = model.names
+
+#         found_human = False
+#         if len(boxes) > 0:
+#             for box in boxes:
+#                 class_id = int(box.cls[0])
+#                 class_name = names[class_id]
+#                 confidence = float(box.conf[0])
+#                 disease_details += f"🔍 {class_name} - {confidence*100:.2f}%\n"
+
+#                 if class_name.lower() in HUMAN_CLASS_NAMES:
+#                     found_human = True
+
+#         if found_human:
+#             disease_status = "🚨 Disease Detected (Human Image)"
+#             play_alert = "disease_alert.mp3"  # make sure this file exists
+#         elif len(boxes) == 0:
+#             disease_status = "✅ No Disease Detected"
+#         else:
+#             disease_status = "🚨 Disease Detected"
+#             play_alert = "disease_alert.mp3"
+
+#     except Exception as e:
+#         disease_status = "❌ Error during detection"
+#         disease_details = str(e)
+
+#     return image_status, disease_status, disease_details, play_alert
+
+# # Gradio UI
+# with gr.Blocks(title="AgroScan - Human Alert + Disease Detection") as demo:
+#     gr.Markdown("""
+#     <div style="text-align: center;">
+#         <h1 style="color: #2e7d32;">🌿 AgroScan: Plant Disease Detector + Human Alert</h1>
+#         <p style="font-size: 16px; color: #555;">
+#             Upload an image of a leaf or a human. If human is detected, an alert will be triggered.
+#         </p>
+#     </div>
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column(scale=1):
+#             image_result = gr.Textbox(label="🖼️ Image Authenticity", interactive=False)
+#             disease_result = gr.Textbox(label="🌱 Disease Status", interactive=False)
+#             detail = gr.Textbox(label="📋 Details", lines=5, interactive=False)
+#             sound_output = gr.Audio(label="🚨 Alert Sound", autoplay=True, interactive=False)
+
+#     submit_btn.click(fn=predict, inputs=image_input,
+#                      outputs=[image_result, disease_result, detail, sound_output])
+
+# demo.launch()
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# import numpy as np
+# from PIL import Image
+
+# # Load the trained YOLOv8 model
+# model = YOLO("best.pt")  # Make sure 'best.pt' is in the same directory
+
+# # Check if image is real (simple check using NumPy array and format)
+# def is_real_image(image):
+#     try:
+#         if image is None:
+#             return False
+#         arr = np.array(image)
+#         if arr.ndim == 3 and arr.shape[2] in [3, 4]:  # RGB or RGBA
+#             return True
+#         return False
+#     except Exception as e:
+#         print(f"Image check error: {e}")
+#         return False
+
+# # Prediction function
+# def predict(image):
+#     image_status = "❌ Fake Image"
+#     disease_status = "⚠️ Unknown"
+#     disease_details = ""
+
+#     # Step 1: Image authenticity check
+#     if is_real_image(image):
+#         image_status = "✅ Real Image"
+#     else:
+#         return image_status, "⚠️ Cannot detect disease in a fake image.", ""
+
+#     # Step 2: YOLOv8 disease detection
+#     try:
+#         results = model(image)
+#         boxes = results[0].boxes
+#         names = model.names
+
+#         if len(boxes) == 0:
+#             disease_status = "✅ No Disease Detected"
+#         else:
+#             disease_status = "🚨 Disease Detected"
+#             for box in boxes:
+#                 class_id = int(box.cls[0])
+#                 class_name = names[class_id]
+#                 confidence = float(box.conf[0])
+#                 disease_details += f"🔬 {class_name} - Confidence: {confidence*100:.2f}%\n"
+#     except Exception as e:
+#         disease_status = "❌ Error during detection"
+#         disease_details = str(e)
+
+#     return image_status, disease_status, disease_details
+
+# # Gradio UI
+# with gr.Blocks(title="AgroScan - Plant Disease & Image Verifier") as demo:
+#     gr.Markdown("""
+#     <div style="text-align: center;">
+#         <h1 style="color: #2e7d32;">🌿 AgroScan: Plant Disease Detector + Image Validator</h1>
+#         <p style="font-size: 16px; color: #555;">
+#             Upload a plant leaf image 🍃 to check if it's real or fake and detect any diseases using YOLOv8.
+#         </p>
+#     </div>
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Leaf Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column(scale=1):
+#             image_result = gr.Textbox(label="🖼️ Image Authenticity", interactive=False)
+#             disease_result = gr.Textbox(label="🌱 Disease Status", interactive=False)
+#             detail = gr.Textbox(label="📋 Detailed Diagnosis", lines=5, interactive=False)
+
+#     with gr.Accordion("ℹ️ What is AgroScan?", open=False):
+#         gr.Markdown("""
+#         AgroScan is an AI-powered detector for identifying plant diseases from leaf images. 
+#         It also verifies whether the image is genuine (real leaf image) or fake to avoid misdiagnosis. 
+#         Designed to support farmers, agronomists, and researchers for better crop care.
+#         """)
+
+#     submit_btn.click(fn=predict, inputs=image_input,
+#                      outputs=[image_result, disease_result, detail])
+
+# demo.launch()
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# from PIL import Image
+
+# # Load YOLO model
+# model = YOLO("best.pt")  # Make sure 'best.pt' is in your directory
+
+# # Function to check if image is real
+# def is_real_image(img):
+#     try:
+#         return isinstance(img, Image.Image) and img.mode in ['RGB', 'RGBA'] and img.size[0] > 50 and img.size[1] > 50
+#     except:
+#         return False
+
+# # Main prediction function with error handling
+# def predict(image):
+#     try:
+#         if image is None:
+#             return "❌ No image provided", "⚠️ Cannot check disease", "Upload a valid image first."
+
+#         # Step 1: Check image authenticity
+#         if is_real_image(image):
+#             image_status = "✅ Real Image"
+#         else:
+#             return "❌ Fake Image", "⚠️ Cannot check disease", "Image doesn't appear real. Try another image."
+
+#         # Step 2: Predict with YOLO model
+#         results = model(image)
+#         names = model.names
+#         boxes = results[0].boxes
+
+#         if len(boxes) == 0:
+#             return image_status, "✅ No Disease Detected", "No signs of disease found."
+        
+#         diagnosis = ""
+#         disease_found = False
+#         for box in boxes:
+#             conf = float(box.conf[0])
+#             if conf > 0.5:
+#                 cls_id = int(box.cls[0])
+#                 disease_name = names[cls_id]
+#                 diagnosis += f"🔬 {disease_name} - Confidence: {conf*100:.2f}%\n"
+#                 disease_found = True
+        
+#         if disease_found:
+#             return image_status, "🚨 Disease Detected", diagnosis
+#         else:
+#             return image_status, "✅ No Disease Detected", "No confident detections."
+
+#     except Exception as e:
+#         # Handle unexpected errors gracefully
+#         return "❌ Error", "❌ Error", f"An error occurred: {str(e)}"
+
+# # Gradio Interface
+# with gr.Blocks(title="AgroScan - Plant Disease & Image Verifier") as demo:
+#     gr.Markdown("## 🌿 AgroScan: Plant Disease Detector + Image Validator")
+
+#     with gr.Row():
+#         with gr.Column():
+#             image_input = gr.Image(type="pil", label="📷 Upload Plant Leaf Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column():
+#             image_result = gr.Textbox(label="🖼️ Image Authenticity", interactive=False)
+#             disease_result = gr.Textbox(label="🌱 Disease Status", interactive=False)
+#             detail = gr.Textbox(label="📋 Detailed Diagnosis", lines=5, interactive=False)
+
+#     submit_btn.click(fn=predict, inputs=image_input, outputs=[image_result, disease_result, detail])
+
+# demo.launch()
+
+
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# from PIL import Image
+# import imghdr
+# import os
+
+# # Load YOLOv5 model
+# model = YOLO("best.pt")  # Ensure your model file 'best.pt' is in the same directory
+
+# # Check if image is real or fake (basic format check)
+# def is_real_image(image):
+#     try:
+#         if image is None:
+#             return False
+#         format_check = imghdr.what(image.fp.name)
+#         return format_check in ['jpeg', 'png']
+#     except:
+#         return False
+
+# # Prediction function
+# def predict(image):
+#     image_status = "❌ Fake Image"
+#     disease_status = "⚠️ Unknown"
+#     disease_details = ""
+
+#     # Step 1: Check if image is real
+#     if is_real_image(image):
+#         image_status = "✅ Real Image"
+#     else:
+#         return image_status, "⚠️ Cannot detect disease in a fake image.", ""
+
+#     # Step 2: Run disease detection
+#     results = model(image)
+#     boxes = results[0].boxes
+#     names = model.names
+
+#     if len(boxes) == 0:
+#         disease_status = "✅ No Disease Detected"
+#     else:
+#         disease_status = "🚨 Disease Detected"
+#         for box in boxes:
+#             class_id = int(box.cls[0])
+#             class_name = names[class_id]
+#             confidence = float(box.conf[0])
+#             disease_details += f"🔬 {class_name} - Confidence: {confidence*100:.2f}%\n"
+
+#     return image_status, disease_status, disease_details
+
+# # UI using Gradio Blocks
+# with gr.Blocks(title="AgroScan - Plant Disease & Image Verifier") as demo:
+#     gr.Markdown("""
+#     <div style="text-align: center;">
+#         <h1 style="color: #2e7d32;">🌿 AgroScan: Plant Disease Detector + Image Validator</h1>
+#         <p style="font-size: 16px; color: #555;">
+#             Upload a plant leaf image 🍃 to check if it's real or fake and detect any diseases using YOLOv5.
+#         </p>
+#     </div>
+#     """)
+
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Leaf Image")
+#             submit_btn = gr.Button("🔍 Analyze")
+#         with gr.Column(scale=1):
+#             image_result = gr.Textbox(label="🖼️ Image Authenticity", interactive=False)
+#             disease_result = gr.Textbox(label="🌱 Disease Status", interactive=False)
+#             detail = gr.Textbox(label="📋 Detailed Diagnosis", lines=5, interactive=False)
+
+#     with gr.Accordion("ℹ️ What is AgroScan?", open=False):
+#         gr.Markdown("""
+#         AgroScan is an AI-powered detector for identifying plant diseases from leaf images. 
+#         It also verifies whether the image is genuine (real leaf image) or fake to avoid misdiagnosis. 
+#         Designed to support farmers, agronomists, and researchers for better crop care.
+#         """)
+
+#     submit_btn.click(fn=predict, inputs=image_input,
+#                      outputs=[image_result, disease_result, detail])
+
+# demo.launch()
+
+
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# from PIL import Image
+
+# # Load YOLOv5 model
+# model = YOLO("best.pt")  # Make sure this file exists in your Hugging Face Space
+
+# # Prediction function
+# def predict(image):
+#     results = model(image)
+#     boxes = results[0].boxes
+#     names = model.names
+#     output = ""
+
+#     if len(boxes) == 0:
+#         output = "✅ Healthy: No disease detected!"
+#     else:
+#         for box in boxes:
+#             class_id = int(box.cls[0])
+#             class_name = names[class_id]
+#             confidence = float(box.conf[0])
+#             output += f"🚨 Detected: {class_name} ({confidence*100:.2f}%)\n"
+#     return output
+
+# # Gradio UI using Blocks
+# with gr.Blocks(title="AgroScan - Plant Disease Detector") as demo:
+#     gr.Markdown("""
+#     <div style="text-align: center;">
+#         <h1 style="color: #2e7d32;">🌿 AgroScan: Plant Disease Detector</h1>
+#         <p style="font-size: 16px; color: #555;">
+#             Upload a high-quality image of a plant leaf 🍃 and detect possible diseases using AI-powered YOLOv5.
+#         </p>
+#     </div>
+#     """)
+    
+#     with gr.Row():
+#         with gr.Column(scale=1):
+#             image_input = gr.Image(type="pil", label="📷 Upload Leaf Image")
+#             predict_button = gr.Button("🔍 Analyze Leaf", variant="primary")
+#         with gr.Column(scale=1):
+#             result_output = gr.Textbox(label="🩺 Diagnosis Result", lines=6)
+
+#     with gr.Accordion("ℹ️ About AgroScan", open=False):
+#         gr.Markdown("""
+#         **AgroScan** is an intelligent plant disease detector built with **YOLOv5** and trained on custom agricultural datasets. 
+#         It helps farmers, researchers, and agriculturists in early detection of plant diseases to ensure timely treatment and improved crop yield. 🌱
+#         """)
+
+#     predict_button.click(fn=predict, inputs=image_input, outputs=result_output)
+
+# demo.launch()
+
+
+
+
+
+
+# from ultralytics import YOLO
+# import gradio as gr
+# from PIL import Image
+# import torch
+
+# # Load YOLOv5 model
+# model = YOLO("best.pt")  # assumes model is in the same directory
+
+# # Prediction function
+# def predict(image):
+#     results = model(image)
+#     boxes = results[0].boxes
+#     names = model.names
+#     output = ""
+
+#     if len(boxes) == 0:
+#         output = "✅ Healthy: No disease detected!"
+#     else:
+#         for box in boxes:
+#             class_id = int(box.cls[0])
+#             class_name = names[class_id]
+#             confidence = float(box.conf[0])
+#             output += f"🚨 Detected: {class_name} ({confidence*100:.2f}%)\n"
+
+#     return output
+
+# # Gradio UI
+# gr.Interface(
+#     fn=predict,
+#     inputs=gr.Image(type="pil"),
+#     outputs="text",
+#     title="🌿 AgroScan: Plant Disease Detector",
+#     description="Upload a plant leaf image to detect disease using a YOLOv5 model.",
+# ).launch()

requirements.txt

@@ -1,4 +1,7 @@
 ultralytics
 gradio
-torch
+numpy
 Pillow
+
+
+