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import streamlit as st
import tensorflow as tf
from transformers import pipeline
import google.generativeai as palm
import os
import json
# Load Dog Breed Classification Model
breed_classification_model = tf.keras.models.load_model('tf_model.h5')
# Load config file
with open('config.json', 'r') as config_file:
config = json.load(config_file)
dogs_breeds = config['breeds']
# Set up Google Generative AI API
google_api_key = os.getenv('AIzaSyCfXkAWLIaWAsfgu1h4s8eAb-AlzGGcEJ0')
palm.configure(api_key=google_api_key)
# Streamlit App
st.title("Dog Breed Classifier and Food Recommender")
# Upload Dog Image
dog_image = st.file_uploader("Upload a picture of your dog", type=["jpg", "jpeg", "png"])
if dog_image is not None:
# Display Dog Image
st.image(dog_image, caption="Uploaded Dog Image.", use_column_width=True)
# Perform Dog Breed Classification
# (Note: You need to preprocess the image appropriately based on your dog breed classification model)
# Replace this with your actual image preprocessing logic
img = tf.io.read_file(dog_image)
tensor = tf.io.decode_image(img, channels=3, dtype=tf.dtypes.float32)
tensor = tf.image.resize(tensor, [299, 299])
input_tensor = tf.expand_dims(tensor, axis=0)
output = breed_classification_model.predict(input_tensor)
confidences = {label: float(output[0][i]) for i, label in enumerate(dogs_breeds)}
breed_prediction = max(confidences, key=confidences.get)
st.write("Predicted Dog Breed:", breed_prediction)
# Perform Food Recommendation
st.subheader("Food Recommendations for Your Dog:")
prompt = f"Give 5-6 Food recommendations for {breed_prediction}?"
food_recommendations = palm.generate_text(
model='models/text-bison-001',
prompt=prompt,
temperature=0.1
)
for recommendation in food_recommendations:
st.write(recommendation['generated_text'])
# Fetch Product Recommendations from Amazon
st.subheader("Amazon Product Recommendations:")
# Replace this with your actual logic for fetching product recommendations from Amazon
# (You may need to use the Amazon Product Advertising API or another method)
amazon_products = fetch_amazon_product_recommendations(breed_prediction)
st.write(amazon_products)
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