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.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
+Image_classify.keras filter=lfs diff=lfs merge=lfs -text

Image_classify.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5dc762a4e7e6c040f55e2d0c34b74ae05b30d73b046469c2d5a58e403d1f0b12
+size 11614324

app.py

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+from flask import Flask, request, jsonify
+import numpy as np
+import tensorflow as tf
+from tensorflow.lite.python.interpreter import Interpreter
+import os
+import google.generativeai as genai
+
+app = Flask(__name__)
+
+# Load the TensorFlow Lite model
+interpreter = Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define categories
+data_cat = ['disposable cups', 'paper', 'plastic bottle']
+img_height, img_width = 224, 224
+
+# Configure Gemini API
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', 'AIzaSyBx0A7BA-nKVZOiVn39JXzdGKgeGQqwAFg')
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Initialize Gemini model
+gemini_model = genai.GenerativeModel('gemini-pro')
+
+@app.route('/predict', methods=['POST'])  
+def predict():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image uploaded"}), 400
+
+    file = request.files['image']
+    try:
+        # Preprocess the image
+        img = tf.image.decode_image(file.read(), channels=3)
+        img = tf.image.resize(img, [img_height, img_width])
+        img_bat = np.expand_dims(img, 0).astype(np.float32)
+
+        # Set input tensor
+        interpreter.set_tensor(input_details[0]['index'], img_bat)
+
+        # Run inference
+        interpreter.invoke()
+
+        # Get the result
+        output_data = interpreter.get_tensor(output_details[0]['index'])
+        predicted_class = data_cat[np.argmax(output_data)]
+        confidence = np.max(output_data) * 100
+
+        # Generate sustainability insights with Gemini API
+        prompt = f"""
+        You are a sustainability-focused AI. Analyze the {predicted_class} (solid dry waste) 
+        and generate the top three innovative, eco-friendly recommendations for repurposing it. 
+        Each recommendation should:
+        - Provide a title
+        - Be practical and easy to implement
+        - Be environmentally beneficial
+        - Include a one or two-sentence explanation
+        Format each recommendation with a clear title followed by the explanation on a new line.
+        """
+        
+        try:
+            # Generate response using the correct method
+            response = gemini_model.generate_content(prompt)
+            insights = response.text.strip()  # Assuming generate_content returns a string or a response with 'text'
+
+        except Exception as e:
+            insights = f"Error generating insights: {str(e)}"
+            print(f"Gemini API error: {str(e)}")  # For debugging
+
+        # Prepare the response
+        return jsonify({
+            "class": predicted_class,
+            "confidence": confidence,
+            "insights": insights
+        })
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(debug=True)