Update app.py

app.py

@@ -1,44 +1,17 @@
 import gradio as gr
-import requests
-import os 
+from transformers import pipeline
 
-##Bloom
-API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-HF_TOKEN = os.environ["HF_TOKEN"]
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+def convert(Sentence):
+    model = pipeline('text-generation', model='bigscience/bloom-1b1')
+    result = model(Sentence)
+    return result[0]['label'])
 
-def generate(prompt): 
-  json_ = {"inputs": prompt,
-            "parameters":
-            {
-            "top_p": 0.9,
-          "temperature": 1.1,
-          "max_new_tokens": 64,
-          "return_full_text": False,
-          }, 
-          "options": 
-          {"use_cache": True,
-          "wait_for_model": True,
-          },}
-  response = requests.post(API_URL, headers=headers, json=json_)
-  output = response.json()
-  output_tmp = output[0]['generated_text']
-  return output_tmp 
+with gr.Blocks() as bls:
+    gr.Markdown("Here is a Text Generation app")
+    with gr.Row():
+        inp = gr.Textbox(label="Type your prompt here and click Run", placeholder='Example: The main cloud services of AWS are:')
+        out = gr.Textbox(label="The sentiment is:")
+    btn = gr.Button("Run")
+    btn.click(fn=convert, inputs=inp, outputs=out)
 
-demo = gr.Blocks()
-
-with demo:
-  gr.Markdown("<h1><center>Article Generation using Bloom</center></h1>")
-  with gr.Row():
-    example_prompt = gr.Radio( [
-    "The main cloud services provided by AWS are: ", 
-    "The pricing of Amazon EC3 is: ", 
-    "The main competitors of AWS are: ",  ], label= "Try these prompts in sequence, or type any text of your choice")
-  
-  with gr.Row():
-    generated_txt = gr.Textbox()
-
-  b1 = gr.Button("Generate SQL")
-  b1.click(generate,inputs=example_prompt, outputs=generated_txt) 
-
-demo.launch(enable_queue=True)
+bls.launch()