import transformers
import datasets
import streamlit as st

# Load the DNA structures dataset
dataset = datasets.load_dataset("protein_structure", data_dir="dataset_archive(1).zip")

# Load the AlphaFold model
model = transformers.AutoModelForSequenceClassification.from_pretrained("alphafold")

# Create a function to predict the structure of a protein
def predict_structure(sequence):
    prediction = model(sequence.unsqueeze(0))[0]
    return prediction

# Create a Streamlit app
st.title("DNA Structures Research")

# Input the DNA sequence
sequence = st.text_input("DNA Sequence")

# Predict the structure of the protein
prediction = predict_structure(sequence)

# Display the prediction
st.write(prediction)

license:llama2 region:us
{"license": "llama2"}
TAGS
#license-llama2 #region-us 

import transformers
import datasets
import streamlit as st

# Load the DNA structures dataset
dataset = datasets.load_dataset("protein_structure", data_dir="dataset_archive(1).zip")

# Load the AlphaFold model
model = transformers.AutoModelForSequenceClassification.from_pretrained("alphafold")

# Create a function to predict the structure of a protein
def predict_structure(sequence):
    prediction = model(sequence.unsqueeze(0))[0]
    return prediction

# Create a Streamlit app
URL("DNA Structures Research")

# Input the DNA sequence
sequence = st.text_input("DNA Sequence")

# Predict the structure of the protein
prediction = predict_structure(sequence)

# Display the prediction
URL(prediction)

# Load the DNA structures dataset
dataset = datasets.load_dataset("protein_structure", data_dir="dataset_archive(1).zip") # Load the AlphaFold model
model = transformers.AutoModelForSequenceClassification.from_pretrained("alphafold") # Create a function to predict the structure of a protein
def predict_structure(sequence):
    prediction = model(sequence.unsqueeze(0))[0]
    return prediction # Create a Streamlit app
URL("DNA Structures Research") # Input the DNA sequence
sequence = st.text_input("DNA Sequence") # Predict the structure of the protein
prediction = predict_structure(sequence) # Display the prediction
URL(prediction)
TAGS
#license-llama2 #region-us 
 # Load the DNA structures dataset
dataset = datasets.load_dataset("protein_structure", data_dir="dataset_archive(1).zip") # Load the AlphaFold model
model = transformers.AutoModelForSequenceClassification.from_pretrained("alphafold") # Create a function to predict the structure of a protein
def predict_structure(sequence):
    prediction = model(sequence.unsqueeze(0))[0]
    return prediction # Create a Streamlit app
URL("DNA Structures Research") # Input the DNA sequence
sequence = st.text_input("DNA Sequence") # Predict the structure of the protein
prediction = predict_structure(sequence) # Display the prediction
URL(prediction)
passage: TAGS
#license-llama2 #region-us 
# Load the DNA structures dataset
dataset = datasets.load_dataset("protein_structure", data_dir="dataset_archive(1).zip")# Load the AlphaFold model
model = transformers.AutoModelForSequenceClassification.from_pretrained("alphafold")# Create a function to predict the structure of a protein
def predict_structure(sequence):
    prediction = model(sequence.unsqueeze(0))[0]
    return prediction# Create a Streamlit app
URL("DNA Structures Research")# Input the DNA sequence
sequence = st.text_input("DNA Sequence")# Predict the structure of the protein
prediction = predict_structure(sequence)# Display the prediction
URL(prediction)