Update app.py

app.py

@@ -1,33 +1,67 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import pipeline, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, AutoTokenizer
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification", model="lqqqqqqqqq/FinetunedModelGr9", return_all_scores=True)
+class CombinedModel:
+    def __init__(self, classifier_model, classifier_tokenizer, summarizer_model, summarizer_tokenizer):
+        self.classifier_model = classifier_model
+        self.classifier_tokenizer = classifier_tokenizer
+        self.summarizer_model = summarizer_model
+        self.summarizer_tokenizer = summarizer_tokenizer
 
-# Streamlit application title
-st.title("Text Classification")
-st.write("Classification for 3 labels: negative, neutral, positive")
+    def classify_and_summarize(self, text):
+        classifier = pipeline("text-classification", model=self.classifier_model, tokenizer=self.classifier_tokenizer, return_all_scores=True)
+        summarizer = pipeline("summarization", model=self.summarizer_model, tokenizer=self.summarizer_tokenizer)
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
-
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    if text.strip():  # Check if text is not empty
-        # Perform text classification on the input text
-        results = classifier(text)[0]
+        # Classify the text
+        classification_results = classifier(text)[0]
 
-        # Display the classification result
+        # Determine the label with the highest score
         max_score = float('-inf')
         max_label = ''
-
-        for result in results:
+        for result in classification_results:
             if result['score'] > max_score:
                 max_score = result['score']
                 max_label = result['label']
 
-        st.write("Text:", text)
-        st.write("Label:", max_label)
-        st.write("Score:", max_score)
-    else:
-        st.write("Please enter some text to classify.")
+        # Summarize the text
+        summary_results = summarizer(text, max_length=50, min_length=25, do_sample=False)
+
+        return max_label, max_score, summary_results[0]['summary_text']
+
+    @classmethod
+    def from_pretrained(cls, classifier_path, summarizer_path):
+        classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_path)
+        classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_path)
+        summarizer_model = AutoModelForSeq2SeqLM.from_pretrained(summarizer_path)
+        summarizer_tokenizer = AutoTokenizer.from_pretrained(summarizer_path)
+        return cls(classifier_model, classifier_tokenizer, summarizer_model, summarizer_tokenizer)
+
+# Load the combined model
+classifier_path = "lqqqqqqqqq/FinetunedModelGr9"
+summarizer_path = "lqqqqqqqqq/SummarizeModelGr9"
+combined_model = CombinedModel.from_pretrained(classifier_path, summarizer_path)
+
+# Streamlit application title
+st.title("Text Classification and Summarization")
+st.write("Classification for 3 labels: negative, neutral, positive")
+
+# Text input for user to enter the text to classify
+texts_input = st.text_area("Enter the texts to classify and summarize (one text per line)", "")
+
+# Perform text classification and summarization when the user clicks the "Classify" button
+if st.button("Classify"):
+    texts = texts_input.split('\n')
+    for text in texts:
+        text = text.strip()
+        if text:  # Check if text is not empty
+            # Classify and summarize the input text
+            label, score, summary = combined_model.classify_and_summarize(text)
+
+            # Display the results
+            st.write("Text:", text)
+            st.write("Label:", label)
+            st.write("Score:", score)
+            st.write("Summary:", summary)
+            st.write("---")
+        else:
+            st.write("Please enter some text to classify and summarize.")