change image path

app.py

@@ -1,303 +1,3 @@
-# import cv2
-# import numpy as np
-# from PIL import Image
-# import torch
-# from torchvision import models, transforms
-# from ultralytics import YOLO
-# import gradio as gr
-# import torch.nn as nn
-# import pandas as pd
-# from io import BytesIO
-
-# # ============================================
-# # RICE ANALYZER PRO
-# # Advanced Grain Analytics and Quality Assessment Platform
-# # ============================================
-
-# # --- SYSTEM CONFIGURATION ---
-# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# # Initialize detection and classification models
-# try:
-#     detection_model = YOLO('best.pt')
-#     classifier_network = models.resnet50(weights=None)
-#     classifier_network.fc = nn.Linear(classifier_network.fc.in_features, 3)
-#     classifier_network.load_state_dict(
-#         torch.load('rice_resnet_model.pth', map_location=device)
-#     )
-#     classifier_network = classifier_network.to(device)
-#     classifier_network.eval()
-#     models_loaded = True
-# except Exception as e:
-#     print(f"Model initialization failed: {e}")
-#     detection_model = None
-#     classifier_network = None
-#     models_loaded = False
-
-# # --- VARIETY DEFINITIONS ---
-# VARIETY_MAP = {
-#     0: "C9 Premium",
-#     1: "Kant Special",
-#     2: "Superfine Grade"
-# }
-
-# VARIETY_COLORS = {
-#     "C9 Premium": (255, 100, 100),      # Red
-#     "Kant Special": (100, 100, 255),    # Blue
-#     "Superfine Grade": (100, 255, 100)  # Green
-# }
-
-# # --- IMAGE PREPROCESSING ---
-# image_preprocessor = transforms.Compose([
-#     transforms.Resize((224, 224)),
-#     transforms.ToTensor(),
-#     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-# ])
-
-# # ============================================
-# # ANALYTICS FUNCTIONS
-# # ============================================
-
-# def classify_grain(grain_image):
-#     """
-#     Classify a single grain using the neural network.
-#     Returns the grain variety label.
-#     """
-#     if not models_loaded:
-#         return "System Error"
-    
-#     tensor_input = image_preprocessor(grain_image).unsqueeze(0).to(device)
-#     with torch.no_grad():
-#         output = classifier_network(tensor_input)
-#         class_idx = torch.argmax(output, dim=1).item()
-#     return VARIETY_MAP[class_idx]
-
-# def generate_distribution_report(variety_counts):
-#     """
-#     Generate a text-based summary of grain variety distribution
-#     with total counts, percentages, and dominant variety.
-#     """
-#     total = sum(variety_counts.values())
-#     if total == 0:
-#         return "No grains detected for analysis."
-    
-#     report = ["## Grain Distribution Report\n"]
-#     report.append(f"Total Grains Detected: **{total}**\n\n")
-#     report.append("### Breakdown by Variety:\n")
-    
-#     for variety, count in sorted(variety_counts.items(), key=lambda x: x[1], reverse=True):
-#         percentage = (count / total) * 100
-#         bar_length = int(percentage / 5)
-#         bar = "█" * bar_length + "░" * (20 - bar_length)
-#         report.append(f"- {variety}: {count}  ({percentage:.1f}%) {bar}\n")
-    
-#     dominant_variety = max(variety_counts.items(), key=lambda x: x[1])[0]
-#     report.append(f"\nDominant Variety: **{dominant_variety}**\n")
-#     return "".join(report)
-
-# def generate_csv_export(grain_details):
-#     """
-#     Convert grain detection results into CSV format for export.
-#     """
-#     if not grain_details:
-#         return None
-    
-#     df = pd.DataFrame(grain_details)
-#     csv_buffer = BytesIO()
-#     df.to_csv(csv_buffer, index=False)
-#     csv_buffer.seek(0)
-#     return csv_buffer.getvalue().decode()
-
-# def analyze_rice_image(input_image):
-#     """
-#     Full analysis pipeline:
-#     1. Detect grains
-#     2. Classify each grain
-#     3. Annotate image
-#     4. Generate distribution report
-#     5. Generate CSV export
-#     """
-#     if not models_loaded:
-#         raise gr.Error("Analysis engine not available. Check model files.")
-    
-#     if input_image is None:
-#         raise gr.Error("Please upload an image to start analysis.")
-    
-#     # Convert PIL image to BGR array for OpenCV
-#     img_array = np.array(input_image)
-#     img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
-    
-#     # Step 1: Detect grains
-#     results = detection_model(img_bgr, verbose=False)[0]
-#     boxes = results.boxes.xyxy.cpu().numpy()
-    
-#     if len(boxes) == 0:
-#         return (
-#             Image.fromarray(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)),
-#             "No grains detected. Try a clearer image.",
-#             None
-#         )
-    
-#     # Step 2: Classify grains
-#     variety_counts = {v: 0 for v in VARIETY_MAP.values()}
-#     grain_details = []
-    
-#     for idx, box in enumerate(boxes):
-#         x1, y1, x2, y2 = map(int, box[:4])
-#         crop = img_bgr[y1:y2, x1:x2]
-        
-#         if crop.shape[0] > 0 and crop.shape[1] > 0:
-#             pil_crop = Image.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB))
-#             variety_label = classify_grain(pil_crop)
-#             variety_counts[variety_label] += 1
-            
-#             # Save details for CSV export
-#             grain_details.append({
-#                 "Grain_ID": f"G{idx+1:04d}",
-#                 "Variety": variety_label,
-#                 "X_center": (x1 + x2)//2,
-#                 "Y_center": (y1 + y2)//2
-#             })
-            
-#             # Annotate image
-#             color = VARIETY_COLORS[variety_label]
-#             cv2.rectangle(img_bgr, (x1, y1), (x2, y2), color, 3)
-#             label = variety_label
-#             (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
-#             cv2.rectangle(img_bgr, (x1, y1-h-10), (x1+w, y1), color, -1)
-#             cv2.putText(img_bgr, label, (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
-    
-#     # Step 3: Generate analytics report
-#     report_text = generate_distribution_report(variety_counts)
-#     csv_export = generate_csv_export(grain_details)
-    
-#     return (
-#         Image.fromarray(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)),
-#         report_text,
-#         csv_export
-#     )
-
-# # ============================================
-# # GRADIO USER INTERFACE
-# # ============================================
-
-# custom_css = """
-# .gradio-container {
-#     font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-# }
-# .header-box {
-#     background: linear-gradient(135deg, #1e5631 0%, #4c9a2a 100%);
-#     padding: 25px;
-#     border-radius: 12px;
-#     color: white;
-#     text-align: center;
-#     margin-bottom: 20px;
-# }
-# """
-
-# with gr.Blocks(css=custom_css, title="Rice Classifier") as app:
-    
-#     gr.HTML("""
-#         <div class="header-box">
-#             <h1>Rice Analyzer Pro</h1>
-#             <p>Advanced Grain Classification | Rice Grain Locattor</p>
-#         </div>
-#     """)
-    
-#     with gr.Tabs():
-#         # Analysis Tab
-#         with gr.Tab("Analysis"):
-#             gr.Markdown("""
-#             ### How to Use
-#             1. Upload a clear image of rice grains.
-#             2. Click "Start Analysis".
-#             3. Review annotated results, distribution report, and export CSV.
-            
-#             **Color Coding:** Red = C9 Premium, Blue = Kant Special, Green = Superfine Grade
-#             """)
-            
-#             with gr.Row():
-#                 with gr.Column(scale=1):
-#                     image_input = gr.Image(type="pil", label="Sample Image")
-#                     start_btn = gr.Button("Start Analysis", variant="primary", size="lg")
-                
-#                 #show the annotated image in specific width and height
-#                 with gr.Column(scale=1):
-#                     annotated_output = gr.Image(label="Annotated Results", height=600, width=600)
-            
-#             with gr.Row():
-#                 report_output = gr.Markdown(label="Distribution Report")
-            
-#             with gr.Row():
-#                 csv_output = gr.Textbox(
-#                     label="CSV Export (Copy or Save)",
-#                     lines=8,
-#                     max_lines=15,
-#                 )
-            
-#             start_btn.click(
-#                 fn=analyze_rice_image,
-#                 inputs=image_input,
-#                 outputs=[annotated_output, report_output, csv_output]
-#             )
-        
-#         # Documentation Tab
-#         with gr.Tab("Documentation"):
-#             gr.Markdown("""
-#             ## System Overview
-            
-#             Rice Classifier uses a deep learning pipeline:
-            
-#             1. **Grain Detection:** YOLO-based model identifies rice grains.
-#             2. **Grain Classification:** ResNet50 model classifies grains into three varieties.
-#             3. **CSV Export:** Detailed grain data available for download or copy.
-            
-#             ### Supported Varieties
-#             | Variety | Description |
-#             |---------|-------------|
-#             | C9 Premium | High-quality long grain |
-#             | Kant Special | Medium grain specialty |
-#             | Superfine Grade | Ultra-refined grain |
-            
-#             ### Best Practices
-#             - Use well-lit images without shadows
-#             - Keep grains separated
-#             - Use plain backgrounds
-#             - Resolution: 1024x1024 or higher for best results
-            
-#             ### Technical Details
-#             - Detection: YOLOv8
-#             - Classification: ResNet50 fine-tuned
-#             - GPU recommended for faster processing
-#             """)
-
-#     gr.Markdown("---")
-#     gr.Markdown("### Sample Gallery")
-    
-#     gr.Examples(
-#         examples=[
-#             "samples/rice1.jpg",
-#             "samples/rice2.jpg",
-#             "samples/rice4.jpg",
-#             "samples/rice5.jpg"
-#         ],
-#         inputs=image_input,
-#         outputs=[annotated_output, report_output, csv_output],
-#         fn=analyze_rice_image,
-#         label="Click any sample to run analysis"
-#     )
-
-# if __name__ == "__main__":
-#     app.queue()
-#     app.launch()
-
-
-
-
-
-
-
-
 import os
 import cv2
 import tempfile
@@ -537,7 +237,7 @@ custom_css = """
 
 # Only include sample images that actually exist on disk
 _all_samples = [
-    "samples/rice1.jpg",
+    "samples/rice3.jpg",
     "samples/rice2.jpg",
     "samples/rice4.jpg",
     "samples/rice5.jpg"