Add application file

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer ,T5ForConditionalGeneration ,T5Tokenizer
+import re
+import torch
+torch.set_default_tensor_type(torch.cuda.FloatTensor)
+import  os
+import io
+import warnings
+from PIL import Image
+from stability_sdk import client
+import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
+import gradio as gr
+
+
+def generate_post(model,tokenizer,company_name , description , example1 ,example2 ,example3): 
+    prompt =  f""" {company_name} {description}, {example1}. 
+                  {company_name} {description}, {example2}.
+                  {company_name} {description}, {example3}.
+                  {company_name} {description}, """
+
+    input_ids = tokenizer(prompt, return_tensors="pt").to(0)
+    sample = model.generate(**input_ids, top_k=0, temperature=0.7, do_sample = True , max_new_tokens = 70, repetition_penalty= 5.4)
+    outputs = tokenizer.decode(sample[0])
+    res = outputs.split(f""" {company_name} {description}, {example1}. 
+                  {company_name} {description}, {example2}.
+                  {company_name} {description}, {example3}.
+                  {company_name} {description}, """)[1]
+
+    res = re.sub('[#]\w+' , " ", res)
+    res = re.sub('@[^\s]\w+',' ', res)
+    res = re.sub(r'http\S+', ' ', res)
+    res = res.replace("\n" ," ")
+    res = re.sub(' +', ' ',res)
+    return  res
+
+def generate_caption(model , text_body ,tokenizer  ,max_length):
+  test_sent = 'generate: ' + text_body
+  input = tokenizer.encode(test_sent ,  return_tensors="pt")#.to('cuda')
+  outs = model.generate(input ,
+                        max_length = max_length,
+                        do_sample = True ,
+                        temperature = 0.7,
+                        min_length = 8,
+                        repetition_penalty = 5.4,
+                        max_time = 12,
+                        top_p = 1.0,
+                        top_k = 50)
+  
+  
+  sent = tokenizer.decode(outs[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
+  return sent 
+
+
+
+def demo_smg(company_name ,description , example1 , example2 , example3):
+    
+  model_cp= T5ForConditionalGeneration.from_pretrained("Abdelmageed95/caption_model")
+  tokenizer = T5Tokenizer.from_pretrained('t5-base')
+
+  access_token = "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"
+  model_bm = AutoModelForCausalLM.from_pretrained("bigscience/bloom-3b" , use_auth_token = access_token)
+  tokenizer_bm = AutoTokenizer.from_pretrained("bigscience/bloom-3b")
+  
+  res = generate_post( model_bm , tokenizer_bm, company_name , description , example1 , example2 , example3)
+  generated_caption = generate_caption( model_cp , 
+                  res,
+                  tokenizer , 
+                  30)
+
+  os.environ['STABILITY_HOST'] = "grpc.stability.ai:443"
+  os.environ['STABILITY_KEY'] = "sk-t4x1wv6WFgTANF7O1TkWDJZzxXxQZeU6X7oybl6rdCOOiHIk"
+  stability_api = client.StabilityInference(
+     key=os.environ['STABILITY_KEY'], 
+     verbose=True)
+  generated_caption = generated_caption + ", intricate, highly detailed, smooth , sharp focus, 8k"
+
+  answers = stability_api.generate( prompt=  generated_caption ,
+    #seed=34567, 
+    steps= 70 )
+  
+
+  for resp in answers:
+      for artifact in resp.artifacts:
+          if artifact.finish_reason == generation.FILTER:
+              warnings.warn(
+                  "Your request activated the API's safety filters and could not be processed."
+                  "Please modify the prompt and try again.")
+          if artifact.type == generation.ARTIFACT_IMAGE:
+              img = Image.open(io.BytesIO(artifact.binary))
+              
+
+  return res, generated_caption  ,img
+
+
+company_name = "ADES Group"
+
+description = "delivers full-scale petroleum services; from onshore and offshore drilling to full oil & gas projects and services, with emphasis on the HSE culture while maintaining excellence in operation targets."
+
+example1 = """Throwback to ADM 680 Team during their Cyber-chair controls Course in August,
+             Our development strategy at ADES does not only focus on enriching the technical expertise of our teams in their specialization in Jack- up rigs, 
+             but also in providing access to latest operational models"""
+
+example2 = """With complexity of oil & gas equipment and the seriousness of failure and its consequences confronting our people, 
+            it has become a necessity to equip our Asset Management Team with leading methodologies and techniques that enable them to think and act proactively"""
+  
+example3 = """ Part of our people development strategy is providing our senior leadership with the latest industry technologies 
+            and world class practices and standards"""
+txt , generated_caption , im = demo_smg( company_name, description , example1 , example2 , example3)
+
+print(txt)
+print(generated_caption)
+
+# demo = gr.Interface(
+#     fn= demo_smg,
+#     inputs=["text","text" , "text" ,"text" ,"text"],
+#     outputs=["text", "text", "image" ]
+# )
+# demo.launch(share=True)

requirements.txt

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+transformers -q
+gradio==3.3.1
+SentencePiece==0.1.97
+stability-sdk==0.2.3