d

app.py

@@ -4,21 +4,24 @@ from transformers import pipeline
 # Load a text generation pipeline with Flan-T5-small model
 llm_pipeline = pipeline(task="text-generation", model="google/flan-t5-small")
 
-# Function to generate text based on user input
+# Function to generate text based on user input, with a system instruction
 def generate_text(prompt):
+    system_instruction = "Respond like a helpful AI: "  # System instruction
+    full_prompt = system_instruction + prompt  # Combine the instruction with user input
+    
     result = llm_pipeline(
-        prompt, 
-        max_length=50,  # Limit the max length of generated text
-        num_return_sequences=1,  # Generate only 1 sequence
-        temperature=0.7,  # Control randomness (lower = less random)
-        top_k=50,  # Limits the sampling pool to the top k tokens
-        top_p=0.9  # Cumulative probability cutoff (for top-p sampling)
+        full_prompt, 
+        max_length=50,  
+        num_return_sequences=1,  
+        temperature=0.7,  
+        top_k=50,  
+        top_p=0.9  
     )
     return result[0]['generated_text']
 
 # Define the Gradio interface
 with gr.Blocks() as gradio_app:
-    gr.Markdown("## Text Generation with Flan-T5-small")
+    gr.Markdown("## Text Generation with Flan-T5-small (Helpful AI Mode)")
     prompt = gr.Textbox(label="Enter your prompt", placeholder="Type something here...")
     output = gr.Textbox(label="Generated text")
     submit_btn = gr.Button("Generate")