Create app.py

app.py

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+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