Delete app.py

app.py

@@ -1,82 +0,0 @@
-import torch 
-import re 
-import gradio as gr
-from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
-import cohere
-import os
-
-
-key_srkian = os.environ["key_srkian"]
-co = cohere.Client(key_srkian)#srkian
-device='cpu'
-encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-
-def predict(department,image,max_length=64, num_beams=4):
-  image = image.convert('RGB')
-  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-  caption_ids = model.generate(image, max_length = max_length)[0]
-  caption_text = clean_text(tokenizer.decode(caption_ids))
-  dept=department
-  context= caption_text
-  response = co.generate(
-  model='large',
-  prompt=f'create non offensive one line meme for given department and context\n\ndepartment- data science\ncontext-a man sitting on a bench with a laptop\nmeme- \"I\'m not a data scientist, but I play one on my laptop.\"\n\ndepartment-startup\ncontext-a young boy is smiling while using a laptop\nmeme-\"When your startup gets funded and you can finally afford a new laptop\"\n\ndepartment- {dept}\ncontext-{context}\nmeme-',
-  max_tokens=20,
-  temperature=0.8,
-  k=0,
-  p=0.75,
-  frequency_penalty=0,
-  presence_penalty=0,
-  stop_sequences=["department"],
-  return_likelihoods='NONE')
-  reponse=response.generations[0].text
-  reponse = reponse.replace("department", "")
-  Feedback_SQL="DEPT"+dept+"CAPT"+caption_text+"MAMAY"+reponse
-  
-     
-  return reponse 
-
-
-
-# input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
- 
- 
- 
-output = gr.outputs.Textbox(type="auto",label="Meme")
-#examples = [f"example{i}.jpg" for i in range(1,7)]
-#examples = os.listdir() 
-examples = [f"example{i}.png" for i in range(1,7)]
-
-#examples=os.listdir()
-#for fichier in examples:
-  #  if not(fichier.endswith(".png")):
-   #     examples.remove(fichier)
-        
-description= " Looking for a fun and easy way to generate memes? Look no further than Meme world! Leveraging large language models like GPT-3PT-3  / Ai21 / Cohere, you can create memes that are sure to be a hit with your friends or network . Created with ♥️ dicuss @[Xaheen](https://chat.whatsapp.com/BA2s37KvPrG4ach28iISBv). kindly share your thoughts in discussion session and use the app responsibly "
-title = "Meme world 🖼️"
-dropdown=["data science ", "product management","marketing","startup" ,"agile","crypto" , "SEO" ]
-
-article = "Created By :  Xaheen "
-
-interface = gr.Interface(
-        fn=predict,
-        inputs = [gr.inputs.Dropdown(dropdown),gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)],
-        
-        theme="grass",
-        outputs=output,
-       examples = examples,#
-        title=title,
-        description=description,
-        article = article,
-    )
-interface.launch(debug=True)
-
-
-# c0here2022