Update app.py

app.py

@@ -3,6 +3,10 @@ from datasets import load_dataset
 from langchain.llms import HuggingFaceEndpoint
 from langchain.prompts import FewShotChatMessagePromptTemplate, ChatPromptTemplate
 from langchain.schema.messages import SystemMessage
+from huggingface_hub import login
+
+# Authenticate using Hugging Face token
+login("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")  # Use your Hugging Face token here
 
 # Set page config at the very top, before anything else
 st.set_page_config(page_title="DialogSum Few-Shot Summarizer", page_icon="🧠")
@@ -44,17 +48,17 @@ if user_input:
     formatted_prompt = few_shot_prompt.format_messages(dialogue=user_input)
     
     # Add formatted examples to the final prompt
-    formatted_prompt = final_prompt.format_messages(dialogue=user_input)
+    final_formatted_prompt = final_prompt.format_messages(dialogue=user_input)
     
     # Convert the list of messages into a single string
-    prompt_string = "\n".join([msg.content for msg in formatted_prompt])  # Access content with .content
+    prompt_string = "\n".join([msg.content for msg in final_formatted_prompt])  # Access content with .content
 
     # Get response from model with correct explicit parameters
     llm = HuggingFaceEndpoint(
         repo_id="google/pegasus-xsum",
-        task="summarization",  # Change to 'summarization'
+        task="summarization",  # Correct task for text summarization
         temperature=0.3,  # Explicitly passing temperature here
-        max_new_tokens=128  # Explicitly passing max_new_tokens here
+        max_length=128  # Explicitly passing max_length here
     )
     
     # Run the LLM with the prompt string