Add application and requirements file

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BartForConditionalGeneration
+
+# Load the models and tokenizers
+@st.cache_resource
+def load_models():
+    t5_model = AutoModelForSeq2SeqLM.from_pretrained("Jiraheya/samsum_model_t5_small_10_epochs")
+    t5_tokenizer = AutoTokenizer.from_pretrained("Jiraheya/samsum_model_t5_small_10_epochs")
+    
+    bart_model = BartForConditionalGeneration.from_pretrained("Jiraheya/pegasus_xsum_samsum_model_10epoch")
+    bart_tokenizer = AutoTokenizer.from_pretrained("Jiraheya/pegasus_xsum_samsum_model_10epoch")
+    
+    return t5_model, t5_tokenizer, bart_model, bart_tokenizer
+
+t5_model, t5_tokenizer, bart_model, bart_tokenizer = load_models()
+
+# Set up the Streamlit app
+st.title("Dialogue Summarizer Chatbot")
+
+# Create a dropdown for model selection
+model_choice = st.selectbox(
+    "Choose a model:",
+    ("T5-small", "BART-large-cnn")
+)
+
+# Create a text area for user input
+user_input = st.text_area("Enter your dialogue here:", height=200)
+
+# Create a button to generate summary
+if st.button("Generate Summary"):
+    if user_input:
+        # Prepare input for the model
+        input_text = "summarize: " + user_input
+        
+        if model_choice == "T5-small":
+            inputs = t5_tokenizer([input_text], max_length=1024, return_tensors="pt")
+            summary_ids = t5_model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=60)
+            summary = t5_tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        else:  # BART-large-cnn
+            inputs = bart_tokenizer([input_text], max_length=1024, return_tensors="pt")
+            summary_ids = bart_model.generate(inputs["input_ids"], num_beams=2, min_length=10, max_length=60)
+            summary = bart_tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+        # Display the summary
+        st.subheader("Summary:")
+        st.write(summary)
+    else:
+        st.warning("Please enter some dialogue to summarize.")
+
+# Add information about the app in the sidebar
+st.sidebar.subheader("About the App")
+st.sidebar.info(
+    "This app uses fine-tuned models to summarize dialogues. "
+    "Choose a model, enter your dialogue in the text area, and click 'Generate Summary' to get a concise summary."
+)
+st.sidebar.markdown("Models available:")
+st.sidebar.markdown("- T5-small: Jiraheya/samsum_model_t5_small_10_epochs")
+st.sidebar.markdown("- BART-large-cnn: Jiraheya/pegasus_xsum_samsum_model_10epoch")

requirements.txt

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+transformers
+torch
+streamlit