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import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import tempfile
import base64
import re
import json
import random_responses
import time
import matplotlib.pyplot as plt
import pathlib
from streamlit_extras.app_logo import add_logo
import zipfile
#Decoracion de la app
st.set_page_config(page_title="Imagen Classification",
page_icon=":turtle:",
layout="wide",
initial_sidebar_state="auto",
menu_items=None)
markdown = """
La app realiza clasificacion de imagenes de las siguientes especies:
- *Gopherus flavomarginatus*
- *Kinosternon flavescens*
- *Terrapene coahuila*
- *Trachemys scripta*
\n
El uso del Chatbot se desplega al clasificar tu imagen
- Algunos ejemplos de pregunta:
- Descripcion de *Gopherus flavomarginatus*
- Distribucion de *Trachemys scripta*
\n
:grey-background[*Developed by Bruno Rodriguez*]
"""
st.sidebar.title("INFORMACION\nV.1.0")
st.sidebar.info(markdown)
logo = "./Clicker.jpg"
st.sidebar.image(logo)
st.markdown("<h1 style='text-align: center;'>ASK MY JORGE</h1>", unsafe_allow_html=True)
#Script main chatbot
def load_json(file):
with open(file) as bot_responses:
# print(f"Loaded '{file}' succesfully!")
return json.load(bot_responses)
responses_data = load_json("./bot.json")
def get_responses(input_string):
split_message = re.split(r'\s+|[,;?!.-]\s*', input_string.lower())
score_list = []
for response in responses_data:
response_score = 0
required_score = 0
required_words = response["required_words"]
if required_words:
for word in split_message:
if word in required_words:
required_score += 1
if response_score == len(required_words):
for word in split_message:
if word in response["user_input"]:
response_score += 1
score_list.append(response_score)
best_response = max(score_list)
response_index = score_list.index(best_response)
if input_string == "":
return "Que quieres saber sobre las tortugas :)"
if best_response != 0:
return responses_data[response_index]["bot_response"]
return random_responses.random_string()
#Tensorflow completo
##Clasificacion de imagen
#nombres de las tortugas
turtle_name = ['Gopherus flavomarginatus', 'Kinisternon flavescens', 'Terrapene coahuila', 'Trachemys scripta']
#carga del modelo
model = tf.keras.models.load_model('./turtle_model_V_1_8.keras')
###########################################
def classify_images(image_path):
input_image = tf.keras.utils.load_img(image_path, target_size= (224, 224))
input_image_array = tf.keras.utils.img_to_array(input_image)
input_image_exp_dim = tf.expand_dims(input_image_array, 0)
prediction = model.predict(input_image_exp_dim)
result = tf.nn.softmax(prediction[0])
outcome = f'Tu imagen es clasificada como: {turtle_name[np.argmax(result)]}'
# # #Grafico de la distribucion de probabilidades
class_turtle = ['Gopherus flavomarginatus',
'Kinosternon flavescens',
'Terrapene coahuila',
'Trachemys scripta']
fig, ax = plt.subplots(figsize=(3, 3))
y_pos = np.arange(len(class_turtle))
ax.barh(y_pos, prediction[0], align = 'center')
ax.set_yticks(y_pos)
ax.set_yticklabels(class_turtle)
ax.invert_yaxis()
ax.set_xlabel("Probality")
ax.set_title("Turtle Classification")
st.pyplot(fig)
return outcome
#Carga de la imagen a clasificar
file = st.file_uploader("Porfavor carga una imagen", type = ["jpg","png"])
if file is not None:
# Create a temporary file to save the uploaded image
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(file.getbuffer())
temp_file_path = temp_file.name
image = Image.open(temp_file_path)
st.image(image, width=200)
# Classify the image using the temporary file path
classification_result = classify_images(temp_file_path)
st.markdown(classification_result)
#auto play de cancion
#https://discuss.streamlit.io/t/how-to-play-an-audio-file-automatically-generated-using-text-to-speech-in-streamlit/33201/2
def autoplay_audio(file_path: str):
with open(file_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode()
md = f"""
<audio controls autoplay="true">
<source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
</audio>
"""
st.markdown(
md,
unsafe_allow_html=True,
)
#st.write("# Auto-playing Audio!")
autoplay_audio("./Finish.mp3")
#chatbot interface
# Interfaz de Streamlit
st.markdown('''### Chatbot de Tortugas 🐢 ''')
st.markdown("""
<style>
.stTextInput input[aria-label="Tú:"] {
background-color: #1db2cc;
color: #000000;
}
</style>
""", unsafe_allow_html=True)
# Caja de texto para entrada del usuario
user_input = st.text_input("Tú:", "")
# Mostrar la respuesta del bot
if user_input:
bot_response = get_responses(user_input)
st.text_area("BugNo:", bot_response, max_chars=None)
else:
#st.text("No has cargado imagen aún!")
st.text("")
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