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import streamlit as st
st.set_page_config(page_title="Turkish Review Analysis - via AG", page_icon='📖')
st.header("📖Movie Review Analysis - TR")
with st.sidebar:
hf_key = st.text_input("HuggingFace Access Key", key="hf_key", type="password")
MODEL_MOVIE = {
"albert": "anilguven/albert_tr_turkish_movie_reviews", # Add the emoji for the Meta-Llama model
"distilbert": "anilguven/distilbert_tr_turkish_movie_reviews",
"bert": "anilguven/bert_tr_turkish_movie_reviews",
"electra": "anilguven/electra_tr_turkish_movie_reviews",
}
MODEL_MOVIES = ["albert","distilbert","bert","electra"]
# Use a pipeline as a high-level helper
from transformers import pipeline
# Create a mapping from formatted model names to their original identifiers
def format_model_name(model_key):
name_parts = model_key
formatted_name = ''.join(name_parts) # Join them into a single string with title case
return formatted_name
formatted_names_to_identifiers = {
format_model_name(key): key for key in MODEL_MOVIE.keys()
}
with st.expander("About this app"):
st.write(f"""
1-Choose your model for movie review analysis (negative or positive).\n
2-Enter your sample text.\n
3-And model predict your text's result.
""")
# Debug to ensure names are formatted correctly
#st.write("Formatted Model Names to Identifiers:", formatted_names_to_identifiers)
model_name: str = st.selectbox("Model", options=MODEL_MOVIES)
selected_model = MODEL_MOVIE[model_name]
if not hf_key:
st.info("Please add your HuggingFace Access Key to continue.")
st.stop()
access_token = hf_key
pipe = pipeline("text-classification", model=selected_model, token=access_token)
#from transformers import AutoTokenizer, AutoModelForSequenceClassification
#tokenizer = AutoTokenizer.from_pretrained(selected_model)
#pipe = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=selected_model)
comment = st.text_input("Enter your text for analysis")#User input
st.text('')
if st.button("Submit for Analysis"):#User Review Button
if not hf_key:
st.info("Please add your HuggingFace Access Key to continue.")
st.stop()
else:
result = pipe(comment)[0]
label=''
if result["label"] == "LABEL_0": label = "Negative"
else: label = "Positive"
st.text(label + " comment with " + str(result["score"]) + " accuracy")
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