Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+
+import gradio as gr
+import requests
+import os 
+import json #
+
+##Bloom
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom-350m"
+# HF_TOKEN = os.environ["HF_TOKEN"]
+# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def translate(prompt_ , from_lang, to_lang, input_prompt = "translate this", seed = 42): 
+
+  prompt =  f"Instruction : Given an {from_lang} input sentence translate it into {to_lang} sentence. \n input : \"{prompt_}\" \n {to_lang} : " 
+  if len(prompt) == 0:
+    prompt = input_prompt 
+    
+  json_ = {
+            "inputs": prompt,
+            "parameters": {
+                            "top_p": 0.9,
+                            "temperature": 1.1,
+                            "max_new_tokens": 250,
+                            "return_full_text": False,
+                            "do_sample": False,
+                            "seed": seed,
+                            "early_stopping": False,
+                            "length_penalty": 0.0,
+                            "eos_token_id": None,
+                          }, 
+          "options": {
+              "use_cache": True,
+              "wait_for_model": True,
+                     },
+        }
+  response = requests.request("POST", API_URL,  json=json_) # headers=headers
+  # output = response.json()
+  output = json.loads(response.content.decode("utf-8"))
+  output_tmp = output[0]['generated_text']
+  solution = output_tmp.split(f"\n{to_lang}:")[0]  
+  
+
+  if '\n\n' in solution:
+    final_solution = solution.split("\n\n")[0] 
+  else:
+    final_solution = solution
+  return final_solution
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("<h1><center>Translate with Bloom</center></h1>")
+  gr.Markdown('''
+## Model Details
+BLOOM is an autoregressive Large Language Model (LLM), trained to continue text 
+from a prompt on vast amounts of text data using industrial-scale computational 
+resources. As such, it is able to output coherent text in 46 languages and 13 
+programming languages that is hardly distinguishable from text written by humans. 
+BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained 
+for, by casting them as text generation tasks.
+
+## Project Details
+In this project we are going to explore the translation capabitlies of "BLOOM".
+
+## How to use
+At the moment this space has only capacity to translate between English, Spanish and Hindi languages.
+from languange is the languge you put in text box and to langauge is to what language you are intended to translate.
+Select from language from the drop down. 
+Select to language from the drop down.
+
+people are encouraged to improve this space by contributing.
+
+this space is created by [Kishore](https://www.linkedin.com/in/kishore-kunisetty-925a3919a/) inorder to participate in [EuroPython22](https://huggingface.co/EuroPython2022)
+please like the project to support my contribution to EuroPython22. 😊
+''')
+  with gr.Row():
+    from_lang = gr.Dropdown(['English', 'Spanish', 'Hindi'], 
+                            value='English', 
+                            label='select From language : ')
+    to_lang = gr.Dropdown(['English', 'Spanish', 'Hindi'], 
+                          value='Hindi', 
+                          label= 'select to Language : ')
+ 
+  input_prompt = gr.Textbox(label="Enter the sentence : ", 
+                            value=f"Instruction: ... \ninput: \"from sentence\" \n{to_lang} :",
+                            lines=6)
+  
+  generated_txt = gr.Textbox(lines=3)
+
+  b1 = gr.Button("translate")
+  b1.click(translate,inputs=[ input_prompt, from_lang, to_lang], outputs=generated_txt) 
+    
+demo.launch(enable_queue=True, debug=True)
+