Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Define function to load models
+@st.cache(allow_output_mutation=True)
+def load_models():
+    classification_model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
+    classification_model = AutoModelForSequenceClassification.from_pretrained(classification_model_name)
+    classification_tokenizer = AutoTokenizer.from_pretrained(classification_model_name, model_max_length=512)
+
+    summarization_model_name = 't5-base'
+    summarization_model = AutoModelForSeq2SeqLM.from_pretrained(summarization_model_name)
+    summarization_tokenizer = AutoTokenizer.from_pretrained(summarization_model_name, model_max_length=512)
+
+    return classification_model, classification_tokenizer, summarization_model, summarization_tokenizer
+
+classification_model, classification_tokenizer, summarization_model, summarization_tokenizer = load_models()
+
+# Title of the app
+st.title('Text Classification and Summarization with Hugging Face')
+
+# Take user input
+text = st.text_area("Enter text:", "")
+submit_button = st.button("Analyze Text")
+
+# Predict function for sentiment analysis
+def predict_sentiment(text):
+    tokenized_text = classification_tokenizer.tokenize(text)
+    results = []
+    
+    # Break text into chunks of max_model_length tokens
+    for i in range(0, len(tokenized_text), classification_tokenizer.model_max_length):
+        chunk = tokenized_text[i:i+classification_tokenizer.model_max_length]
+        chunk = classification_tokenizer.convert_tokens_to_string(chunk)
+
+        inputs = classification_tokenizer(chunk, return_tensors="pt", truncation=True, padding='max_length')
+        outputs = classification_model(**inputs)
+        probs = torch.nn.functional.softmax(outputs[0], dim=-1)
+        results.append(probs.detach().numpy())
+    return results
+
+# Predict function for text summarization
+def summarize_text(text):
+    tokenized_text = summarization_tokenizer.tokenize(text)
+    summaries = []
+    
+    # Break text into chunks of max_model_length tokens
+    for i in range(0, len(tokenized_text), summarization_tokenizer.model_max_length):
+        chunk = tokenized_text[i:i+summarization_tokenizer.model_max_length]
+        chunk = summarization_tokenizer.convert_tokens_to_string(chunk)
+        
+        inputs = summarization_tokenizer.encode("summarize: " + chunk, return_tensors="pt", truncation=True, padding='max_length')
+        outputs = summarization_model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+        summary = summarization_tokenizer.decode(outputs[0]).replace('<pad>', '').replace('</s>', '')
+        summaries.append(summary)
+    return summaries
+
+if submit_button:
+    if text:
+        with st.spinner("Analyzing..."):
+            # Sentiment analysis
+            results = predict_sentiment(text)
+            for i, probs in enumerate(results):
+                st.markdown(f"**Result {i+1}:**")
+                st.markdown(f"**Positive sentiment:** `{probs[0][1]:.2f}`")
+                st.markdown(f"**Negative sentiment:** `{probs[0][0]:.2f}`")
+
+            # Text summarization
+            summaries = summarize_text(text)
+            for i, summary in enumerate(summaries):
+                st.markdown(f"**Summary {i+1}:** `{summary}`")
+    else:
+        st.warning("Please enter text to analyze.")