Upload 1_Movie_Reviews.py

pages/1_Movie_Reviews.py

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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")
+
+