update to timestamp aware version

app.py

@@ -18,9 +18,8 @@ pipe = pipeline(
 
 print("Model loaded successfully!")
 
-def format_chat_template(document):
+def format_chat_template(instruction, document):
     """Format input using the recommended template from model card"""
-    instruction = "Please summarize the input document."
     row_json = [{
         "role": "user", 
         "content": f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{document}\n\n### Response:\n"
@@ -28,18 +27,18 @@ def format_chat_template(document):
     return tokenizer.apply_chat_template(row_json, tokenize=False, add_generation_prompt=False)
 
 @spaces.GPU
-def summarize(text):
-    """Generate summary using the model"""
+def summarize(instruction, text):
+    """Generate summary using the model with custom instruction"""
     try:
-        # Format input with recommended template
-        formatted_input = format_chat_template(text)
+        # Format input with custom instruction
+        formatted_input = format_chat_template(instruction, text)
         
         # Generate summary
         output = pipe(
             formatted_input,
-            max_new_tokens=2000,
+            max_new_tokens=512,
             do_sample=True,
-            temperature=0.3,
+            temperature=0.7,
             top_p=0.9,
             return_full_text=False
         )
@@ -54,20 +53,52 @@ def summarize(text):
 # Create Gradio interface
 demo = gr.Interface(
     fn=summarize,
-    inputs=gr.Textbox(
-        lines=10, 
-        placeholder="Enter text to summarize...",
-        label="Input Text"
-    ),
+    inputs=[
+        gr.Textbox(
+            lines=3,
+            value="Please summarize this meeting transcript, preserving key timestamps and noting when important topics were discussed.",
+            label="Custom Instruction",
+            placeholder="Enter your instruction here..."
+        ),
+        gr.Textbox(
+            lines=10, 
+            placeholder="Enter text or meeting transcript to summarize...",
+            label="Document/Transcript"
+        )
+    ],
     outputs=gr.Textbox(
         label="Summary",
-        lines=5
+        lines=8
     ),
     title="SummLlama3.2-3B Summarization",
-    description="Test the DISLab/SummLlama3.2-3B model - a specialized summarization model trained with DPO",
+    description="Test the DISLab/SummLlama3.2-3B model with customizable instructions. Modify the instruction to control how the model summarizes your content.",
     examples=[
-        ["Artificial intelligence has made remarkable progress in recent years, particularly in natural language processing. Large language models can now understand context, generate human-like text, and perform complex reasoning tasks. These advances have enabled applications ranging from chatbots to code generation tools, transforming how we interact with technology."]
-    ]
+        [
+            "Please summarize this meeting transcript, preserving key timestamps and noting when important topics were discussed.",
+            """Alvaro Orsi   1:39
+Yeah.
+Mohammad Hossain Dehghan Shoar   1:47
+What that policy does XY and said, what is the impact on each agent and what is the impact on their sick leaves and overall life expectancy? So I think I wanna spend a little bit more time to make it more exciting how agents interact with one another and make it more general in terms of.
+Alvaro Orsi   1:57
+Yeah.
+Yeah.
+Mohammad Hossain Dehghan Shoar   2:07
+Infectious disease as well because at the moment it is set four PM 2.5, but I think it's it's the more difficult to run in versus grad ABM. But I've I think it's it's running, we're getting the same similar results at least."""
+        ],
+        [
+            "Summarize the key technical points discussed in this transcript.",
+            """Artificial intelligence has made remarkable progress in recent years, particularly in natural language processing. Large language models can now understand context, generate human-like text, and perform complex reasoning tasks. These advances have enabled applications ranging from chatbots to code generation tools, transforming how we interact with technology."""
+        ],
+        [
+            "Extract action items and decisions from this meeting, including who is responsible and any mentioned timeframes.",
+            """Team Meeting - Project Alpha
+John (9:15): We need to finalize the API design by Friday.
+Sarah (9:20): I'll take ownership of the authentication module. Can deliver by Thursday.
+Mike (9:25): The database schema needs review. John, can you look at it by Wednesday?
+John (9:27): Sure, I'll review it tomorrow and get back to you."""
+        ]
+    ],
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":