app inference complete

app.py

@@ -1,10 +1,8 @@
 import streamlit as st
 from pathlib import Path
 import base64
-#
-#import robustnessgym as rg
-from PIL import Image
-
+from transformers import pipeline, set_seed
+from huggingface_hub.inference_api import InferenceApi
 
 
 # Initial page config
@@ -31,9 +29,13 @@ def img_to_bytes(img_path):
 
 # sidebar
 
-#def load_model():
-   # model_out = pipeline(task="text-generation", model="distilgpt2")
-    #return model_out
+def load_model():
+    generator = pipeline('text-generation', model='distilgpt2')
+    set_seed(48)
+    text = st.text_input('Provide an initial text prompt')
+
+    if text != '' :
+        out = generator(text, max_length=30, num_return_sequences=1)
 
 def cs_sidebar():
 
@@ -167,56 +169,6 @@ OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/mas
 
   ''')
 
-  #  How to Get Started
-
-    col1.subheader('How to Get Started')
-    with col1.expander(""):
-        st.markdown('''
-
-
-
-*Be sure to read the sections on in-scope and out-of-scope uses and limitations of the model for further information on how to use the model.*
-
-Using DistilGPT2 is similar to using GPT-2. DistilGPT2 can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
-
-```python
->>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='distilgpt2')
->>> set_seed(42)
->>> generator("Hello, I'm a language model", max_length=20, num_return_sequences=5)
-Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
-[{'generated_text': "Hello, I'm a language model, I'm a language model. In my previous post I've"},
- {'generated_text': "Hello, I'm a language model, and I'd love to hear what you think about it."},
- {'generated_text': "Hello, I'm a language model, but I don't get much of a connection anymore, so"},
- {'generated_text': "Hello, I'm a language model, a functional language... It's not an example, and that"},
- {'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I"}]
-``` 
-  
- 
-**Here is how to use this model to get the features of a given text in PyTorch**:
-
-```python
-from transformers import GPT2Tokenizer, GPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
-model = GPT2Model.from_pretrained('distilgpt2')
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-**And in TensorFlow:**
-
-```python
-from transformers import GPT2Tokenizer, TFGPT2Model
-tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
-model = TFGPT2Model.from_pretrained('distilgpt2')
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```
-
-    ''')
-
 
     # Training Data
 
@@ -274,17 +226,16 @@ GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGP
 
     ''')
 
-    
-
-    # Try App
-
-    col2.header('Try App')
-    col2.code('''[To:do add integration with HF
-    ''')
+   
+   ################################
+   ## Column 2: right most column
+   ################################
+  
+ 
 
     #  How to Get Started
 
-    with col2.header('How to Get Started'):
+    with col2.subheader('How to Get Started'):
         col2.markdown(''' 
          *Be sure to read the sections on in-scope and out-of-scope uses and limitations of the model for further information on how to use the model.*
         ''')
@@ -331,23 +282,27 @@ output = model(encoded_input)
 
             ''')
 
-    # Visuals
 
+    # Try App
     
+    col2.header('Try out DistilGP2')
+    #print load_model()
+    with col2.subheader(''):
+        generator = pipeline('text-generation', model='distilgpt2')
+        set_seed(48)
+        text = st.text_input('Text Generation: Provide an initial text prompt')
+        if text != '' :
+            out = generator(text, max_length=30, num_return_sequences=1)
+            col2.write(out)
+    
+ 
 
+    # Contact Section
 
-
-
-    #pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-    # Placeholders, help, and options
-
-    col2.subheader('Placeholders, help, and anything else')
-    #pipeline = load_model()
-
-    col2.code('''
-
-    ''')
+    with col2.header('Further Contact'):
+        url = "https://huggingface.co/spaces/Ezi/ModelCardsAnalysis/discussions"
+        col2.markdown("Further contact, input and/or questions are welcomed 🤗 [here](%s)" % url)
+    
 
 
     

requirements.txt

@@ -1,3 +1,3 @@
 transformers
 torch
-transformers-interpret
+transformers-interpret