Update app.py

app.py

@@ -1,15 +1,92 @@
+import os
+import re
+
 import gradio as gr
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+import openai
+
+
+
+openai.api_key = os.environ['OPENAI_KEY']
+
+
+## Training models
+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)
+
+
+## READING THE IMAGE
+## Extracting features from image
+## then create a context for the image like
+## Then input the department and context extracted and send it to LLM to get captio meme
+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)
+  print(image)
+  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
+  print(clean_text)
+  caption_ids = model.generate(image, max_length = max_length)[0]
+  print(caption_ids)
+  caption_text = clean_text(tokenizer.decode(caption_ids))
+  print(caption_text)
+  dept=department
+  context= caption_text
+  response = openai.Completion.create(
+                model="text-davinci-003",
+                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)
+  reponse = response.choices[0].text
+  reponse = reponse.replace("department", "")
+  Feedback_SQL="DEPT"+dept+"CAPT"+caption_text+"MAMAY"+reponse
+  
+     
+  return reponse 
+
 
+
+
+ 
+ 
+ 
+output = gr.outputs.Textbox(type="text",label="Meme")
+
+examples = [f"example{i}.png" for i in range(1,7)]
+
+
+## GRADIO INTERFACE     
+
+
+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 ♥️ by Arsalan @[Xaheen](https://www.linkedin.com/in/sallu-mandya/). kindly share your thoughts in discussion session and use the app responsibly #NO_Offense \n \n built with ❤️   @[Xhaheen](https://www.linkedin.com/in/sallu-mandya/)"
+title = "Meme world 🖼️"
+dropdown=["data science", "product management","marketing","startup" ,"agile","crypto" , "SEO" ]
+
+article = "Created By :  Xaheen "
 theme = gr.themes.Glass(
     primary_hue="cyan",
     
-    neutral_hue="gray",
+    neutral_hue="gray",font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
 )
 
 interface = gr.Interface(
-        fn=lambda x: x,
-        inputs = [gr.inputs.Textbox()],
-        outputs = [gr.inputs.Textbox()],
-        theme=theme)
-
-interface.launch()
+        fn=predict,
+        inputs = [gr.inputs.Dropdown(dropdown),gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)],
+        
+        theme=theme,
+        outputs=output,
+        examples =[['data science', 'example5.png'],
+            ['product management', 'example2.png'],
+            ['startup', 'example3.png'],
+            ['marketing', 'example4.png'],
+            ['agile', 'example1.png'],
+            ['crypto', 'example6.png']],
+        title=title,
+        description=description,
+        article = article
+    )
+interface.launch(debug=True)