Update app.py

app.py

@@ -1,3 +1,45 @@
-import gradio as gr    
-iface = gr.Interface.load("huggingface/bigscience/bloom-1b1", examples=[['The main cloud services provided by AWS are '], ['The pricing of Amazon S3 is ']])
-iface.launch()
+import gradio as gr
+import requests
+import os 
+
+##Bloom
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
+HF_TOKEN = os.environ["HF_TOKEN"]
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+
+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 
+
+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)