Create app.py

app.py

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+from transformers import BartForConditionalGeneration, BartTokenizer
+
+# Load the pre-trained BART model and tokenizer
+model_name = "facebook/bart-large-cnn"
+model = BartForConditionalGeneration.from_pretrained(model_name)
+tokenizer = BartTokenizer.from_pretrained(model_name)
+
+# Define a function to summarize a conversation
+def summarize_conversation(conversation):
+    # Join the conversation into a single text (separate sentences by a space)
+    conversation_text = " ".join(conversation)
+
+    # Tokenize the conversation
+    inputs = tokenizer(conversation_text, return_tensors="pt", max_length=1024, truncation=True)
+
+    # Generate the summary
+    summary_ids = model.generate(inputs['input_ids'], num_beams=4, min_length=50, max_length=200, early_stopping=True)
+
+    # Decode the summary
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Sample conversation between agent and customer
+conversation = [
+    "Hello, how can I assist you today?",
+    "I need help with my order. I haven't received it yet.",
+    "Can you provide me with your order number?",
+    "Sure, my order number is 123456.",
+    "Thank you. Let me check the status of your order.",
+    "It looks like your order was delayed due to some shipping issues.",
+    "When will I receive my order?",
+    "The order is expected to arrive within the next 2-3 days."
+]
+
+# Generate summary
+summary = summarize_conversation(conversation)
+
+# Print the summary
+print("Conversation Summary:")
+print(summary)