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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)

	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)