update

app.py

@@ -31,11 +31,16 @@ A: Let’s think step by step.
 """
 
 
-def text_generate(prompt):
+def text_generate(prompt, problem, template):
   
-  #prints for debug
-  print(f"*****Inside text_generate function - Prompt is :{prompt}")
-  json_ = {"inputs": prompt,
+  #prints to debug
+  print(f"*****Inside text_generate function******")
+  print(f"Prompt is :{prompt}")
+  print(f"Problem is :{prompt}")
+  print(f"Template is :{prompt}")
+  
+  p = problem + "A: " + template + "\n"
+  json_ = {"inputs": p,
             "parameters":
             {
             "top_p": 0.9,
@@ -60,15 +65,21 @@ demo = gr.Blocks()
 with demo:
   gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
   gr.Markdown(
-        """ [BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. Some amazing researchers( [Jason Wei et al.](https://arxiv.org/abs/2206.07682)) recently found out that by addding **Lets think step by step** it improves the model's zero-shot performance. Some might say — You can get good results out of LLMs if you know how to speak to them.\n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma)"""
+        """ [BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. Some amazing researchers( [Jason Wei et al.](https://arxiv.org/abs/2205.11916)) recently found out that by addding **Lets think step by step** it improves the model's zero-shot performance. Some might say — You can get good results out of LLMs if you know how to speak to them.\n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo."""
         )
   with gr.Row():
+
     example_prompt = gr.Radio( ["Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\nA: Let’s think step by step.\n", "Q: Roger has 5 tennis balls already. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\nA: Let’s think step by step.\n", "Q: On an average Joe throws 25 punches per minute. His fight lasts 5 rounds of 3 minutes each. How many punches did he throw?\nA:  Let’s think about this logically.\n"], label= "Choose a sample Prompt")
+
+    example_problem = gr.Radio( ["Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\n", "Q: Roger has 5 tennis balls already. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\n", "Q: On an average Joe throws 25 punches per minute. His fight lasts 5 rounds of 3 minutes each. How many punches did he throw?\n"], label= "Choose a sample Prompt")
+
+    example_template = gr.Radio( ["Let’s think step by step."," First, ", " Let’s think about this logically.", "Let’s solve this problem by splitting it into steps.", " Let’s be realistic and think step by step.", "Let’s think like a detective step by step.", "Let’s think", "Before we dive into the answer,", "The answer is after the proof."], label= "Choose a sample Template for Zero-Shot CoT")
+
     #input_word = gr.Textbox(placeholder="Enter a word here to generate text ...")
     generated_txt = gr.Textbox(lines=7)
  
   
   b1 = gr.Button("Generate Text")
-  b1.click(text_generate,inputs=example_prompt, outputs=generated_txt) 
+  b1.click(text_generate,inputs=[example_prompt, example_problem, example_template], outputs=generated_txt) 
  
 demo.launch(enable_queue=True, debug=True)