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README (1).md

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+---
+title: Sentiment Analysis App
+emoji: 🌖
+colorFrom: blue
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (3).py

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+import streamlit as st
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, 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
+
+# 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.")

requirements (3).txt

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+streamlit
+torch
+transformers
+pandas