Update app.py

app.py

@@ -25,7 +25,7 @@ from examples import (
 )
 
 repo_id = "meta/llama-3.1-405b-instruct"
-llm_client = ChatNVIDIA(model=repo_id)
+llm_client = ChatNVIDIA(model=repo_id, max_tokens=2000)
 end_sequence = "I hope that helps!"
 
 def generate_key_points(text):
@@ -178,8 +178,28 @@ def create_map_from_markers(dataframe):
 
 def run_display(text):
     current_output = ""
-    for output in generate_key_points(text):
-        current_output += output
+    prompt = f"""             
+Please generate a set of key geographical points for the following description: {text}, as a json list of less than 10 dictionnaries with the following keys: 'name', 'description'.
+ALWAYS precise the city and country in the 'name'. For instance do not only "name": "Notre Dame" as the name but "name": "Notre Dame, Paris, France".
+Generally try to minimize the distance between locations. Always think of the transportation means that you want to use, and the timing: morning, afternoon, where to sleep.
+Only generate two sections: 'Thought:' provides your rationale for generating the points, then you list the locations in 'Key points:'.
+Then generate '{end_sequence}' to indicate the end of the response.
+
+For instance:
+Description: {description_sf}
+Thought: {output_example_sf}
+{end_sequence}
+
+Description: {description_loire}
+Thought: {output_example_loire}
+{end_sequence}
+
+Now begin. You can make the descriptions a bit more verbose than in the examples.
+
+Description: {text}
+Thought:"""
+    for output in llm_client.stream(prompt):
+        current_output += output.content
         yield None, "```text\n" + current_output + "\n```"
     current_output = current_output.replace("</s>", "")
     dataframe, _ = parse_llm_output(current_output)
@@ -227,4 +247,4 @@ with gr.Blocks(
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(debug=True)