Update app.py

app.py

@@ -1,12 +1,11 @@
- 
-#import os
-
-
-
 import torch 
 import re 
 import gradio as gr
 from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+import cohere
+
+
+
 
 device='cpu'
 encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
@@ -17,28 +16,50 @@ tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
 model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
 
 
-def predict(image,max_length=64, num_beams=4):
+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))
-  return caption_text 
+  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="Captions")
+# 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() 
-description= "Image captioning application made using transformers"
-title = "Image Captioning 🖼️"
+description= "meme generation using advanced NLP "
+title = "Meme world 🖼️"
+dropdown=["data science ", "product management","marketing","startup" ,"agile","crypto" , "SEO" ]
 
-article = "Created By : Shreyas Dixit "
+article = "Created By :  Xaheen "
 
 interface = gr.Interface(
         fn=predict,
-        inputs = input,
+        inputs = [gr.inputs.Dropdown(dropdown),gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)],
+        
         theme="grass",
         outputs=output,
      #   examples = examples,
@@ -46,4 +67,7 @@ interface = gr.Interface(
         description=description,
         article = article,
     )
-interface.launch(debug=True)
+interface.launch(debug=True)
+
+
+# c0here2022