v1 of the app

app.py

@@ -17,7 +17,7 @@ def describe_photo(image):
 def generate_story(description):
     model = GPT2LMHeadModel.from_pretrained("gpt2")
     tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    inputs = tokenizer.encode(description + " [SEP] A funny and friendly story:", return_tensors='pt')
+    inputs = tokenizer.encode(description + ".A funny and friendly story.", return_tensors='pt')
     outputs = model.generate(input_ids=inputs, 
                              max_length=200, 
                              num_return_sequences=1, 

requirements.txt

@@ -10,10 +10,10 @@ scikit-learn
 nltk
 gensim
 tensorflow
-keras
 opencv-python
 fastapi
 uvicorn
 gTTS
 openai-clip
-gradio
+gradio
+tf-keras