Update app.py

app.py

@@ -4,10 +4,10 @@ import time
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Streamlit page configuration
-st.set_page_config(page_title="Review Keypoint Extractor", page_icon="🔑")
+st.set_page_config(page_title="Review Keypoint Extractor (MeetingSummary)", page_icon="🔑")
 
 # Define the model
-model_name = "t5-small"
+model_name = "knkarthick/MEETING_SUMMARY"
 
 # Cache the model and tokenizer to avoid reloading
 @st.cache_resource
@@ -24,8 +24,12 @@ def generate_keypoint(review, max_new_tokens=64):
     
     start_time = time.time()
     
-    # T5-specific prompt
-    prompt = f"summarize: {review}"
+    # Prompt specific for MEETING_SUMMARY
+    prompt = (
+        "Extract the key points (both strengths and weaknesses) from the following review.\n"
+        'If there are no key points, say "No key point".\n\n'
+        f"{review}"
+    )
     
     # Inference
     inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True).to(device)
@@ -41,8 +45,8 @@ def generate_keypoint(review, max_new_tokens=64):
     return keypoint, elapsed
 
 # Streamlit UI
-st.title("🔑 Review Keypoint Extractor")
-st.write("Enter a product review below to extract its key points using the T5-Small model.")
+st.title("🔑 Review Keypoint Extractor (MeetingSummary)")
+st.write("Enter a product review below to extract its key points using the knkarthick/MEETING_SUMMARY model.")
 
 # Input field for review
 review = st.text_area("Product Review", placeholder="e.g., The Jackery power station is lightweight and charges quickly, but the battery life could be longer.")