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from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import tensorflow as tf
import numpy as np
import google.generativeai as genai
import os
app = FastAPI()
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Configure Gemini API
GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', 'AIzaSyBx0A7BA-nKVZOiVn39JXzdGKgeGQqwAFg')
genai.configure(api_key=GEMINI_API_KEY)
gemini_model = genai.GenerativeModel('gemini-pro')
# 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']
img_height, img_width = 224, 224
def generate_recycling_insight(detected_object):
"""Generate sustainability insights for detected objects"""
try:
prompt = f"""
You are a sustainability-focused AI. Analyze the {detected_object} (which is a solid dry waste)
and generate the top three innovative, eco-friendly recommendations for repurposing it. Ensure each recommendation is:
- Give the Title of the recommendation
- Practical and easy to implement
- Environmentally beneficial
- Clearly explained in one or two concise sentences
"""
response = gemini_model.generate_content(prompt)
return response.text.strip()
except Exception as e:
return f"Error generating insight: {str(e)}"
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
# Read and preprocess the image
contents = await file.read()
image = tf.image.decode_image(contents, channels=3)
image = tf.image.resize(image, [img_height, img_width])
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)
# Make prediction
predictions = model.predict(image, verbose=0)
score = tf.nn.softmax(predictions[0])
confidence = float(np.max(score) * 100)
if confidence < 45:
return {
"error": "Confidence too low to make a prediction",
"confidence": confidence
}
predicted_class = data_cat[np.argmax(score)]
sustainability_insight = generate_recycling_insight(predicted_class)
return {
"class": predicted_class,
"confidence": confidence,
"insights": sustainability_insight
}
except Exception as e:
return {"error": str(e)}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |