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import streamlit as st
from PIL import Image
import pytesseract
import pandas_ta as ta
from textblob import TextBlob
import pandas as pd
import requests
from sklearn.linear_model import LinearRegression
# Helper Functions
def search_web(query):
from googlesearch import search
st.subheader("Resultados de la Búsqueda Web")
results = []
for result in search(query, num_results=5):
results.append(result)
for idx, link in enumerate(results):
st.write(f"{idx + 1}. {link}")
def analyze_image(uploaded_file):
st.subheader("Análisis de Imagen")
image = Image.open(uploaded_file)
st.image(image, caption="Imagen cargada", use_column_width=True)
text = pytesseract.image_to_string(image)
st.write("Texto extraído de la imagen:")
st.write(text)
def analyze_crypto_data(df):
st.subheader("Análisis Técnico")
df['RSI'] = ta.rsi(df['close'], length=14)
macd = ta.macd(df['close'])
df['MACD'], df['MACD_signal'], df['MACD_hist'] = macd['MACD_12_26_9'], macd['MACDs_12_26_9'], macd['MACDh_12_26_9']
bbands = ta.bbands(df['close'])
df['BB_Lower'], df['BB_Mid'], df['BB_Upper'] = bbands['BBL_20_2.0'], bbands['BBM_20_2.0'], bbands['BBU_20_2.0']
st.write(df.tail(10))
def analyze_sentiment(text):
analysis = TextBlob(text)
sentiment = analysis.sentiment.polarity
if sentiment > 0:
return "Positivo"
elif sentiment < 0:
return "Negativo"
else:
return "Neutral"
def predict_prices(df):
st.subheader("Predicción de Precios")
X = df.index.values.reshape(-1, 1)
y = df['close']
model = LinearRegression()
model.fit(X, y)
future = pd.DataFrame({"Index": range(len(df), len(df) + 5)})
predictions = model.predict(future)
st.write("Predicciones de precios futuros:", predictions)
def fetch_crypto_data():
url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart?vs_currency=usd&days=30&interval=daily"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
prices = [item[1] for item in data['prices']]
df = pd.DataFrame(prices, columns=['close'])
return df
else:
st.error("Error al obtener datos de criptomonedas.")
return None
def chat_interface():
st.header("Chat Interactivo")
user_input = st.text_input("Escribe tu mensaje aquí:")
if user_input:
st.write(f"Tú: {user_input}")
# Aquí agregarías lógica para procesar la entrada y responder
st.write("Chatbot: Lo siento, estoy aprendiendo a responder.")
# Main Application
def main():
st.title("Aplicación de Criptomonedas")
menu = ["Chat", "Búsqueda Web", "Análisis de Imágenes", "Análisis Técnico", "Análisis de Sentimiento", "Predicción de Precios"]
choice = st.sidebar.selectbox("Seleccione una opción", menu)
if choice == "Chat":
chat_interface()
elif choice == "Búsqueda Web":
query = st.text_input("Ingrese su búsqueda:")
if query:
search_web(query)
elif choice == "Análisis de Imágenes":
uploaded_file = st.file_uploader("Suba una imagen", type=["png", "jpg", "jpeg"])
if uploaded_file:
analyze_image(uploaded_file)
elif choice == "Análisis Técnico":
df = fetch_crypto_data()
if df is not None:
analyze_crypto_data(df)
elif choice == "Análisis de Sentimiento":
text = st.text_area("Ingrese el texto para analizar:")
if text:
sentiment = analyze_sentiment(text)
st.write(f"El sentimiento del texto es: {sentiment}")
elif choice == "Predicción de Precios":
df = fetch_crypto_data()
if df is not None:
predict_prices(df)
if __name__ == "__main__":
main()
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