update app

app.py

@@ -1,7 +1,87 @@
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import gradio as gr
+import os
 
-def greet(name):
-	return "Hello " + name + "!!"
+model_name = 'ashleychen/bart-finetuning'
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+access_token = os.environ.get('private_token')
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name, use_auth_token=access_token)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token)
+
+def create_prompt(
+    n_followers, n_friends, verified, retweets, favorites, in_US
+):
+    tweet = f"Generate review: in US: {in_US}, retweets: {retweets}, favorites: {favorites}, user followers: {n_followers}, user friends: {n_friends}, user verified: {verified}"
+    return tweet
+
+def postprocess(review):
+    # dot = review.rfind('.')
+    # return review[:dot+1]
+    return review
+
+def generate_reviews(n_followers, n_friends, verified, retweets, favourites, in_US):
+    text = create_prompt(n_followers, n_friends, verified, retweets, favourites, in_US)
+    inputs = tokenizer(text, return_tensors='pt')
+    out = model.generate(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        do_sample=True,
+        num_return_sequences=5,
+        temperature=1.5,
+        top_p=0.9
+    )
+    reviews = []
+    for review in out:
+        reviews.append(postprocess(tokenizer.decode(review, skip_special_tokens=True)))
+
+    return reviews[0], reviews[1], reviews[2], reviews[3], reviews[4]
+
+css = """
+    #ctr {text-align: center;}
+    #btn {color: white; background: linear-gradient(to right, #1CD8D2 0%, #93EDC7  51%, #1CD8D2  100%);}
+"""
+
+
+md_text = """<h1 style='text-align: center; margin-bottom: 1rem'>Generating Pfizer vaccine tweets with BART-base</h1>
+"""
+
+resources = """## Resources
+- The Pfizer Vaccine Tweets dataset can be found [here](https://www.kaggle.com/datasets/gpreda/pfizer-vaccine-tweets)."""
+
+demo = gr.Blocks(css=css)
+
+with demo:
+    with gr.Row():
+        gr.Markdown(md_text)
+    
+    with gr.Row():
+        n_followers = gr.inputs.Slider(minimum=0, maximum=2000, step=100, default=100, label="# user followers")
+        n_friends = gr.inputs.Slider(minimum=0, maximum=5000, step=100, default=2000, label="# user friends")
+        n_favourites = gr.inputs.Slider(minimum=0, maximum=5000, step=100, default=2000, label="# user favourites")
+    with gr.Row():
+        verified = gr.Radio(["True", "False"], label="user verified ✓")
+        retweets = gr.inputs.Slider(minimum=0, maximum=10, step=1, default=0, label="# retweets")
+        favourites = gr.inputs.Slider(minimum=0, maximum=5, step=1, default=0, label="# favourites")
+    with gr.Row():
+        button = gr.Button("Generate tweets !", elem_id='btn')
+
+    with gr.Row():
+        output1 = gr.Textbox(label="Review #1")
+        output2 = gr.Textbox(label="Review #2")
+        output3 = gr.Textbox(label="Review #3")
+        output4 = gr.Textbox(label="Review #4")
+        output5 = gr.Textbox(label="Review #5")
+
+    with gr.Row():
+        gr.Markdown(resources)
+
+    button.click(
+        fn=generate_reviews,
+        inputs=[n_followers, n_friends, n_favourites, verified, retweets, favourites],
+        outputs=[output1, output2, output3, output4, output5]
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow
+torch
+transformers