Update app.py

app.py

@@ -1,149 +1,13 @@
 import streamlit as st
-import pandas as pd
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from transformers import pipeline
 
-# Function to load the pre-trained model
-
-def load_finetune_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    return tokenizer, model
-
-def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    sentiment_pipeline = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
-    return sentiment_pipeline
+# Load the sentiment analysis pipeline using the selected model
+nlp = pipeline("sentiment-analysis", model="distilbert-base-uncased")
 
 # Streamlit app
-st.title("Multi-label Toxicity Detection App")
-st.write("Enter a text and select the fine-tuned model to get the toxicity analysis.")
-
-# Input text
-default_text = "You might be the most stupid person in the world."
-text = st.text_input("Enter your text:", value=default_text)
-
-category = {'LABEL_0': 'toxic', 'LABEL_1': 'severe_toxic', 'LABEL_2': 'obscene', 'LABEL_3': 'threat', 'LABEL_4': 'insult', 'LABEL_5': 'identity_hate'}
-
-
-# Model selection
-model_options = {
-    "Olivernyu/finetuned_bert_base_uncased": {
-        "description": "This model detects different types of toxicity like threats, obscenity, insults, and identity-based hate in text. The table is prepopulated with some data, the table will be displayed once you hit analyze.",
-    },
-    "distilbert-base-uncased-finetuned-sst-2-english": {
-        "labels": ["NEGATIVE", "POSITIVE"],
-        "description": "This model classifies text into positive or negative sentiment. It is based on DistilBERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.",
-    },
-    "textattack/bert-base-uncased-SST-2": {
-        "labels": ["LABEL_0", "LABEL_1"],
-        "description": "This model classifies text into positive(LABEL_1) or negative(LABEL_0) sentiment. It is based on BERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.",
-    },
-    "cardiffnlp/twitter-roberta-base-sentiment": {
-        "labels": ["LABEL_0", "LABEL_1", "LABEL_2"],
-        "description": "This model classifies tweets into negative (LABEL_0), neutral(LABEL_1), or positive(LABEL_2) sentiment. It is based on RoBERTa and fine-tuned on a large dataset of tweets.",
-    },
-}
-selected_model = st.selectbox("Choose a fine-tuned model:", model_options)
-
-st.write("### Model Information")
-st.write(f"**Description:** {model_options[selected_model]['description']}")
-
-initial_table_df = pd.DataFrame(columns=["Text (portion)", "Toxicity class 1", "Class 1 probability", "Toxicity class 2", "Class 2 probability"])
-initial_table_data = [{'Text (portion)': ["who's speaking? \n you goddamn cocksucker you know "],
-  'Toxicity class 1': ['obscene'],
-  'Class 1 probability': 0.7282997369766235,
-  'Toxicity class 2': ['toxic'],
-  'Class 2 probability': 0.2139672487974167},
- {'Text (portion)': ['::Here is another source: Melissa Sue Halverson (2'],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.24484945833683014,
-  'Toxicity class 2': ['obscene'],
-  'Class 2 probability': 0.1627064049243927},
- {'Text (portion)': [', 8 November 2007 (UTC) \n\n All I can say is, havin'],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.7277262806892395,
-  'Toxicity class 2': ['obscene'],
-  'Class 2 probability': 0.2502792477607727},
- {'Text (portion)': ['::::I only see that at birth two persons are given'],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.2711867094039917,
-  'Toxicity class 2': ['insult'],
-  'Class 2 probability': 0.15477754175662994},
- {'Text (portion)': ["* There you have it: one man's Barnstar is another"],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.5408656001091003,
-  'Toxicity class 2': ['insult'],
-  'Class 2 probability': 0.12563346326351166},
- {'Text (portion)': ['" \n\n == Fact == \n\n Could just be abit of trivial f'],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.35239243507385254,
-  'Toxicity class 2': ['obscene'],
-  'Class 2 probability': 0.1686778962612152},
- {'Text (portion)': ['HE IS A GHAY ASS FUCKER@@!!'],
-  'Toxicity class 1': ['obscene'],
-  'Class 1 probability': 0.7819343209266663,
-  'Toxicity class 2': ['toxic'],
-  'Class 2 probability': 0.16951803863048553},
- {'Text (portion)': ["I'VE SEEN YOUR CRIMES AGAINST CHILDREN AND I'M ASH"],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.8491994738578796,
-  'Toxicity class 2': ['threat'],
-  'Class 2 probability': 0.04749392718076706},
- {'Text (portion)': [':While with a lot of that essay says, general time'],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.282654732465744,
-  'Toxicity class 2': ['obscene'],
-  'Class 2 probability': 0.15901680290699005},
- {'Text (portion)': ['== Help == \n\n Please members of wiki, help me. My '],
-  'Toxicity class 1': ['toxic'],
-  'Class 1 probability': 0.3118911385536194,
-  'Toxicity class 2': ['obscene'],
-  'Class 2 probability': 0.16506287455558777}]
-for d in initial_table_data:
-    initial_table_df = pd.concat([initial_table_df, pd.DataFrame(d)], ignore_index=True)
-# Load the model and perform toxicity analysis
-
-if "table" not in st.session_state:
-    st.session_state['table'] = initial_table_df
-
-if st.button("Analyze"):
-    if not text:
-        st.write("Please enter a text.")
-    else:
-        with st.spinner("Analyzing toxicity..."):
-            if selected_model == "Olivernyu/finetuned_bert_base_uncased":
-                toxicity_detector = load_model(selected_model)
-                outputs = toxicity_detector(text, top_k=2)
-                category_names = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
-                results = []
-                for item in outputs:
-                    results.append((category[item['label']], item['score']))
-
-                # Create a table with the input text (or a portion of it), the highest toxicity class, and its probability
-                table_data = {
-                    "Text (portion)": [text[:50]],
-                    "Toxicity class 1": [results[0][0]],
-                    f"Class 1 probability": results[0][1],
-                    "Toxicity class 2": [results[1][0]],
-                    f"Class 2 probability": results[1][1]
-                }
-                # print("Before concatenation:")
-                # print(table_df)
-                # Concatenate the new data frame with the existing data frame
-                st.session_state['table'] = pd.concat([pd.DataFrame(table_data), st.session_state['table']], ignore_index=True)
-                # print("After concatenation:")
-                # print(table_df)
-                # Update the table with the new data frame
-                st.table(st.session_state['table'])
-            else:
-                st.empty()
-                sentiment_pipeline = load_model(selected_model)
-                result = sentiment_pipeline(text)
-                st.write(f"Sentiment: {result[0]['label']} (confidence: {result[0]['score']:.2f})")
-                if result[0]['label'] in ['POSITIVE', 'LABEL_1'] and result[0]['score']> 0.9:
-                    st.balloons()
-                elif result[0]['label'] in ['NEGATIVE', 'LABEL_0'] and result[0]['score']> 0.9:
-                    st.error("Hater detected.")
-else:
-    st.write("Enter a text and click 'Analyze' to perform toxicity analysis.")
+st.title("Sentiment Analysis App")
+user_input = st.text_input("Enter a sentence for sentiment analysis:")
+if user_input:
+    result = nlp(user_input)
+    sentiment = result[0]["label"]
+    st.write(f"Sentiment: {sentiment}")