updating

app.py

@@ -8,23 +8,8 @@ from datetime import datetime
 import csv
 import os
 from huggingface_hub import hf_hub_download
-# import zipfile
 
 
-# # Step 1: Download the ZIP file from your Hugging Face repo
-# zip_path = hf_hub_download(
-#     repo_id="LeemahLee/recycle",  # Your model repo ID
-#     filename="efficientnet_finetuned_2.keras"  # Make sure this is the exact file name
-# )
-
-# # Step 2: Unzip only if not already extracted
-# if not os.path.exists("recycle_model_tf"):
-#     with zipfile.ZipFile(zip_path, "r") as zip_ref:
-#         zip_ref.extractall("recycle_model_tf")
-
-# Step 3: Load the model from the unzipped directory
-# model = load_model("recycle_model_tf/content/recycle_model_tf")
-
 model_path = hf_hub_download(
     repo_id="LeemahLee/recycle",
     filename="recycle_efficientnet_finetuned_2.keras"
@@ -32,212 +17,6 @@ model_path = hf_hub_download(
 
 model = load_model(model_path)
 
-# model = load_model("recycle/efficientnet_finetuned_2.keras")
-# print("✅ Model loaded successfully.")
-# print(model.summary())  # This will print the model architecture to confirm it's loaded.
-
-
-# # Class labels
-# class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light bulbs', 'metal', 'organic', 'paper', 'plastic']
-
-# # Descriptions
-# descriptions = {
-#     'batteries': 'Hazardous waste. Recycle at battery collection points.',
-#     'clothes': 'Reusable or recyclable textiles. Donate or use textile recycling bins.',
-#     'e-waste': 'Electronic waste. Contains toxic components. Use certified e-waste recyclers.',
-#     'glass': 'Non-biodegradable. Place in glass recycling containers.',
-#     'light bulbs': 'May contain mercury. Dispose at special light bulb collection points.',
-#     'metal': 'Recyclable material. Sort into metal recycling containers.',
-#     'organic': 'Biodegradable. Compost or dispose in organic waste bins.',
-#     'paper': 'Recyclable if clean. Place in paper recycling bins.',
-#     'plastic': 'Non-biodegradable. Sort into plastic recycling bins.'
-# }
-
-# # Sample images
-# sample_images = [
-#     "sample_data/10537723_web1_M-Light-Bulb-EDH-180212.jpg",
-#     "sample_data/bag-full-plastic-waste-bottle-plastic-ground-bag-full-plastic-waste-bottle-collect-around-river-145777987.jpg",
-#     "sample_data/assorted-clothes-isolated-heap-colorful-white-36145930.jpg",
-#     "sample_data/aa-batteries-energy-household-appliances-battery-recycling-used-alkaline-batteries-aa-size-format-207672475.jpg",
-#     "sample_data/banana-peel-white-background-composting-organic-waste-banana-peel-white-background-composting-organic-waste-210728827.jpg"
-    
-# ]
-
-# # Feedback log file
-# FEEDBACK_FILE = "user_feedback.csv"
-
-# # Prediction logic
-# # def predict(img):
-# #     try:
-# # def predict(img):
-# #     try:
-# #         if img is None:
-# #             return "❌ No image uploaded.", "", 0.0, ""
-
-# #         img = img.convert("RGB")
-# #         img = img.resize((224, 224))
-# #         img_array = image.img_to_array(img)
-# #         img_array = np.expand_dims(img_array, axis=0)
-# #         img_array = preprocess_input(img_array)
-
-# #         predictions = model.predict(img_array)
-# #         predicted_index = np.argmax(predictions[0])
-# #         predicted_label = class_names[predicted_index]
-# #         confidence = float(np.max(predictions[0]))
-# #         advice = descriptions[predicted_label]
-
-# #         result_text = f"{predicted_label.upper()} ({confidence*100:.2f}%)\n\n{advice}"
-# #         if confidence < 0.50:
-# #             result_text += "\n\n⚠️ Low confidence. Try a clearer image or better lighting."
-
-# #         return result_text, predicted_label, confidence, advice
-# #     except Exception:
-# #         return "❌ No image detected. Please upload or take a photo.", "", 0.0, ""
-
-
-# # def predict(img):
-# #     try:
-# #         if img is None:
-# #             print("❌ No image received.")
-# #             return "❌ No image uploaded.", "", 0.0, ""
-
-# #         print("✅ Image received.")
-# #         img = img.convert("RGB")
-# #         img = img.resize((224, 224))
-# #         img_array = image.img_to_array(img)
-# #         img_array = np.expand_dims(img_array, axis=0)
-# #         img_array = preprocess_input(img_array)
-
-# #         print("✅ Image preprocessed. Shape:", img_array.shape)
-
-# #         predictions = model.predict(img_array)
-# #         print("✅ Model prediction completed:", predictions)
-
-# #         predicted_index = np.argmax(predictions[0])
-# #         predicted_label = class_names[predicted_index]
-# #         confidence = float(np.max(predictions[0]))
-# #         advice = descriptions[predicted_label]
-
-# #         result_text = f"{predicted_label.upper()} ({confidence*100:.2f}%)\n\n{advice}"
-# #         if confidence < 0.50:
-# #             result_text += "\n\n⚠️ Low confidence. Try a clearer image or better lighting."
-
-# #         return result_text, predicted_label, confidence, advice
-
-# #     except Exception as e:
-# #         print("❌ Error during prediction:", str(e))
-# #         return "❌ Error during prediction.", "", 0.0, ""
-
-# def predict(img):
-#     try:
-#         if img is None:
-#             return "❌ No image uploaded.", "", 0.0, ""
-
-#         # Preprocess image
-#         img = img.convert("RGB")
-#         img = img.resize((224, 224))
-#         img_array = image.img_to_array(img)
-#         img_array = np.expand_dims(img_array, axis=0)
-#         img_array = preprocess_input(img_array)
-#         input_tensor = tf.convert_to_tensor(img_array, dtype=tf.float32)
-
-#         # Use the model's serving function for inference
-#         infer = model.signatures["serving_default"]
-#         output = infer(input_tensor)
-
-#         # Get the prediction from the output dictionary
-#         predictions = list(output.values())[0].numpy()
-#         predicted_index = np.argmax(predictions[0])
-#         predicted_label = class_names[predicted_index]
-#         confidence = float(np.max(predictions[0]))
-#         advice = descriptions[predicted_label]
-
-#         result_text = f"{predicted_label.upper()} ({confidence*100:.2f}%)\n\n{advice}"
-#         if confidence < 0.50:
-#             result_text += "\n\n⚠️ Low confidence. Try a clearer image or better lighting."
-
-#         return result_text, predicted_label, confidence, advice
-#     except Exception as e:
-#         return f"❌ Prediction error: {str(e)}", "", 0.0, ""
-
-
-
-# # Save feedback
-# def save_feedback(label, confidence, advice, correct):
-#     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-#     file_exists = os.path.isfile(FEEDBACK_FILE)
-
-#     with open(FEEDBACK_FILE, mode="a", newline="", encoding="utf-8") as file:
-#         writer = csv.writer(file)
-#         if not file_exists:
-#             writer.writerow(["timestamp", "predicted_label", "confidence_percentage", "advice", "correct_prediction"])
-#         writer.writerow([timestamp, label, f"{confidence*100:.2f}%", advice, correct])
-#     return "✅ Feedback received! Thanks for helping us improve."
-
-# # 🚫 No extra background styles here
-# with gr.Blocks() as demo:
-#     gr.Markdown("# ♻️ Waste Classifier")
-#     gr.Markdown("Upload or take a photo of waste to classify it and receive disposal guidance.")
-
-#     with gr.Row():
-#         img_input = gr.Image(
-#             label="📸 Take or Upload a Photo",
-#             type="pil",
-#             sources=["upload", "webcam"],
-#             streaming=False
-#         )
-#         output_text = gr.Textbox(label="Prediction & Advice", lines=5)
-
-#     predicted_label_state = gr.State()
-#     confidence_state = gr.State()
-#     advice_state = gr.State()
-
-#     def handle_prediction(img):
-#         text, label, confidence, advice = predict(img)
-#         return text, label, confidence, advice
-
-#     btn = gr.Button("🔍 Classify")
-#     btn.click(
-#         fn=handle_prediction,
-#         inputs=img_input,
-#         outputs=[output_text, predicted_label_state, confidence_state, advice_state]
-#     )
-
-#     gr.Markdown("### 📝 Was this prediction correct?")
-#     feedback_status = gr.Textbox(label="", interactive=False)
-
-#     with gr.Row():
-#         yes_btn = gr.Button("👍 Yes")
-#         no_btn = gr.Button("👎 No")
-
-#     yes_btn.click(
-#         fn=lambda label, conf, adv: save_feedback(label, conf, adv, "yes"),
-#         inputs=[predicted_label_state, confidence_state, advice_state],
-#         outputs=feedback_status
-#     )
-
-#     no_btn.click(
-#         fn=lambda label, conf, adv: save_feedback(label, conf, adv, "no"),
-#         inputs=[predicted_label_state, confidence_state, advice_state],
-#         outputs=feedback_status
-#     )
-
-#     gr.Markdown("### 🌍 Try with Sample Images")
-#     gr.Gallery(
-#         value=sample_images,
-#         label="Sample Waste Images",
-#         columns=5,
-#         object_fit="contain",
-#         height=230 ,
-#         interactive = False
-#     )
-
-#     gr.Markdown("**Note:** All predictions are currently in English. Multilingual support coming soon!")
-
-# demo.launch()
-
-
-
 # Class labels
 class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs', 'metal', 'organic', 'paper', 'plastic']
 
@@ -256,12 +35,6 @@ descriptions = {
 
 # Sample images
 sample_images = [
-    # "sample_data/10537723_web1_M-Light-Bulb-EDH-180212.jpg",
-    # "sample_data/458-4583586_cardboard-recycling-png-paper-waste-clipart-transparent-png.png",
-    # "sample_data/assorted-clothes-isolated-heap-colorful-white-36145930.jpg",
-    # "sample_data/aa-batteries-energy-household-appliances-battery-recycling-used-alkaline-batteries-aa-size-format-207672475.jpg",
-    # "sample_data/banana-peel-white-background-composting-organic-waste-banana-peel-white-background-composting-organic-waste-210728827.jpg"
-
     "sample_data/10537723_web1_M-Light-Bulb-EDH-180212.jpg",
     "sample_data/bag-full-plastic-waste-bottle-plastic-ground-bag-full-plastic-waste-bottle-collect-around-river-145777987.jpg",
     "sample_data/assorted-clothes-isolated-heap-colorful-white-36145930.jpg",