Create app.py

app.py

@@ -0,0 +1,100 @@
+import json
+import requests
+import gradio as gr
+import random
+import time
+import os
+import datetime
+from datetime import datetime
+
+
+API_TOKEN = os.getenv("API_TOKEN")
+DECODEM_TOKEN=os.getenv("DECODEM_TOKEN")
+
+
+from huggingface_hub import InferenceApi
+inference = InferenceApi("bigscience/bloom",token=API_TOKEN)
+
+headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
+url_decodemprompts='https://us-central1-createinsightsproject.cloudfunctions.net/getdecodemprompts'
+
+data={"prompt_type":'market_size',"decodem_token":DECODEM_TOKEN}
+try:
+    r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers)
+except requests.exceptions.ReadTimeout as e:
+    print(e)
+#print(r.content)
+
+prompt=str(r.content, 'UTF-8')
+
+
+def infer(prompt,
+          max_length = 250,
+          top_k = 0,
+          num_beams = 0,
+          no_repeat_ngram_size = 2,
+          top_p = 0.9,
+          seed=42,
+          temperature=0.7,
+          greedy_decoding = False,
+          return_full_text = False):
+    
+    print(seed)
+    top_k = None if top_k == 0 else top_k
+    do_sample = False if num_beams > 0 else not greedy_decoding
+    num_beams = None if (greedy_decoding or num_beams == 0) else num_beams
+    no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size
+    top_p = None if num_beams else top_p
+    early_stopping = None if num_beams is None else num_beams > 0
+
+    params = {
+        "max_new_tokens": max_length,
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature,
+        "do_sample": do_sample,
+        "seed": seed,
+        "early_stopping":early_stopping,
+        "no_repeat_ngram_size":no_repeat_ngram_size,
+        "num_beams":num_beams,
+        "return_full_text":return_full_text
+    }
+    
+    s = time.time()
+    response = inference(prompt, params=params)
+    #print(response)
+    proc_time = time.time()-s
+    #print(f"Processing time was {proc_time} seconds")
+    return response
+
+def getideas(text_inp):
+  print(text_inp)
+  print(datetime.today().strftime("%d-%m-%Y"))
+  
+  text = prompt+"\nInput:"+text_inp + "\nOutput:"
+  resp = infer(text,seed=random.randint(0,100))
+
+  generated_text=resp[0]['generated_text']
+  result = generated_text.replace(text,'').strip()
+  result = result.replace("Output:","")
+  parts = result.split("###")
+  topic = parts[0].strip()
+  topic="\n".join(topic.split('\n')[:3])
+  print(topic)
+  return(topic)
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>Market Sizing Framework for Your Business</center></h1>")
+    gr.Markdown(
+        """ChatGPT based Insights from <a href="https://www.decodem.ai">Decodem.ai</a> for businesses.\nWhile ChatGPT has multiple use cases we have evolved specific use cases/ templates for businesses \n\n This template provides ideas on how a business can size a market they are entering. Enter a business area to size and get the results. Use examples as a guide. We use a equally powerful AI model bigscience/bloom."""
+        )
+    textbox = gr.Textbox(placeholder="Enter market size focus for business here...", lines=1,label='Your business area')
+    btn = gr.Button("Generate")    
+    output1 = gr.Textbox(lines=2,label='The future')
+
+    btn.click(getideas,inputs=[textbox], outputs=[output1])
+    examples = gr.Examples(examples=['icecream parlor in London','HR saas for fintech','book shops in NYC','Starbucks cafe in Bangalore','organic vegetables via ecommerce','grocery delivery'],
+                           inputs=[textbox])
+    
+
+demo.launch()