update app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import requests
+import os 
+
+##Bloom
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
+HF_TOKEN = os.environ["HF_TOKEN"]
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+#Testing various prompts initially
+prompt1 = """
+word: risk
+poem using word: And then the day came,
+when the risk
+to remain tight
+in a bud
+was more painful
+than the risk
+it took
+to blossom.
+word: """
+
+prompt2 = """
+Q: Joy has 5 balls. He buys 2 more cans of balls. Each can has 3 balls. How many balls he has now?
+A: Joy had 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. Answer is 11.
+Q: Jane has 16 balls. Half balls are golf balls, and half golf balls are red. How many red golf balls are there?
+A: """
+
+prompt3 = """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?
+A: Let’s think step by step.
+"""
+
+
+def text_generate(prompt):
+  
+  #prints for debug
+  print(f"*****Inside poem_generate - Prompt is :{prompt}")
+  json_ = {"inputs": prompt,
+            "parameters":
+            {
+            "top_p": 0.9,
+          "temperature": 1.1,
+          "max_new_tokens": 250,
+          "return_full_text": True
+          }}
+  response = requests.post(API_URL, headers=headers, json=json_)
+  print(f"Response  is : {response}")
+  output = response.json()
+  print(f"output is : {output}") #{output}")
+  output_tmp = output[0]['generated_text']
+  print(f"output_tmp is: {output_tmp}")
+  solution = output_tmp.split("\nQ:")[0]   #output[0]['generated_text'].split("Q:")[0] # +"."
+  print(f"Final response after splits is: {solution}")
+ 
+  return solution 
+
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
+  gr.Markdown(
+        """ BigScienceW Bloom \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. Some amazing researchers 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"""
+        )
+  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"], label= "Choose a sample Prompt")
+    #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) 
+ 
+demo.launch(enable_queue=True, debug=True)