trial

app.py

@@ -106,11 +106,13 @@ def create_interface():
         # Generate Text with LLM
         def generate_text_with_llm(output_prompt, long_talk, compress, compression_level, custom_base_prompt, provider, api_key, model_selected):
             global selected_prompt_type
+            poster = False  # Set a default value or modify as needed
             result = llm_node.generate(
                 input_text=output_prompt,
                 long_talk=long_talk,
                 compress=compress,
                 compression_level=compression_level,
+                poster=poster,  # Added the missing 'poster' argument
                 prompt_type=selected_prompt_type,
                 custom_base_prompt=custom_base_prompt,
                 provider=provider,
@@ -131,4 +133,4 @@ def create_interface():
 
 if __name__ == "__main__":
     demo = create_interface()
-    demo.launch()
+    demo.launch(share=True)

llm_inference.py

@@ -18,6 +18,22 @@ class LLMInferenceNode:
             base_url="https://api.sambanova.ai/v1",
         )
 
+    def generate_prompt(self, dynamic_seed, prompt_type, custom_input):
+        """
+        Generates a prompt based on the provided seed, prompt type, and custom input.
+        """
+        import random
+
+        random.seed(dynamic_seed)
+        if custom_input and custom_input.strip():
+            prompt = custom_input
+        else:
+            prompt = f"Generate a {prompt_type.lower()} prompt."
+
+        # Additional logic can be added here if needed
+        print(f"Generated prompt: {prompt}")  # Debug statement
+        return prompt
+
     def generate(
         self,
         input_text,