Update app.py

app.py

@@ -2,7 +2,6 @@ from fastapi import FastAPI, File, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 import tensorflow as tf
 import numpy as np
-from tensorflow.lite.python.interpreter import Interpreter
 import google.generativeai as genai
 import os
 
@@ -22,13 +21,12 @@ GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', 'AIzaSyBx0A7BA-nKVZOiVn39JXzdGKgeGQ
 genai.configure(api_key=GEMINI_API_KEY)
 gemini_model = genai.GenerativeModel('gemini-pro')
 
-# Load TFLite 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()
+# Load model with specific version handling
+model = tf.keras.models.load_model(
+    'Image_classify.keras',
+    custom_objects=None,
+    compile=False  # Don't compile the model on load
+)
 
 # Define categories and image dimensions
 data_cat = ['disposable cups', 'paper', 'plastic bottle']
@@ -61,17 +59,10 @@ async def predict(file: UploadFile = File(...)):
         image = tf.cast(image, tf.float32)
         image = tf.expand_dims(image, 0)
 
-        # Set the input tensor
-        interpreter.set_tensor(input_details[0]['index'], image)
-
-        # Run inference
-        interpreter.invoke()
-
-        # Get the output tensor
-        output_data = interpreter.get_tensor(output_details[0]['index'])
-        
-        # Calculate confidence and get prediction
-        confidence = float(np.max(output_data) * 100)
+        # Make prediction
+        predictions = model.predict(image, verbose=0)
+        score = tf.nn.softmax(predictions[0])
+        confidence = float(np.max(score) * 100)
         
         if confidence < 45:
             return {
@@ -79,7 +70,7 @@ async def predict(file: UploadFile = File(...)):
                 "confidence": confidence
             }
         
-        predicted_class = data_cat[np.argmax(output_data)]
+        predicted_class = data_cat[np.argmax(score)]
         sustainability_insight = generate_recycling_insight(predicted_class)
         
         return {