Update app.py

app.py

@@ -1,95 +1,52 @@
-# -*- coding: utf-8 -*-
-
 import gradio as gr
 import requests
-import os 
-import json #
+import json
+import os
 
-##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): 
+LANGUAGES = ['Akan', 'Arabic', ' Assamese', 'Bambara', 'Bengali', 'Catalan', 'English', 'Spanish', ' Basque', 'French', ' Gujarati', 'Hindi', 
+'Indonesian', 'Igbo', 'Kikuyu', 'Kannada', 'Ganda', 'Lingala', 'Malayalam', 'Marathi', 'Nepali', 'Chichewa', 'Oriya', 'Panjabi', 'Portuguese', 
+'Kirundi', 'Kinyarwanda', 'Shona', 'Sotho', 'Swahili', 'Tamil', 'Telugu', 'Tswana', 'Tsonga', 'Twi', 'Urdu', 'Viêt Namese', 'Wolof', 'Xhosa', 
+'Yoruba', 'Chinese', 'Zulu']
 
-  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]  
-  
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
 
-  if '\n\n' in solution:
-    final_solution = solution.split("\n\n")[0] 
-  else:
-    final_solution = solution
-  return final_solution
 
-demo = gr.Blocks()
+def translate(input, output, text):
+    """Translate text from input language to output language"""
 
-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.
+    instruction =  f"""Translation in {input.lower()}: {text}\nTranslation in {output.lower()}: """ 
 
-## Project Details
-In this project we are going to explore the translation capabitlies of "BLOOM".
+    json_ = {
+        "inputs": instruction,
+        "parameters": {
+            "return_full_text": True,
+            "do_sample": False,
+        }, 
+        "options": {
+            "use_cache": True,
+            "wait_for_model": True,
+            },
+    }
+    response = requests.request("POST", API_URL,  json=json_) 
+    output = response.json()[0]['generated_text']
 
-## 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.
+    return output.replace(instruction, '', 1)[1:]
 
-people are encouraged to improve this space by contributing.
+demo = gr.Blocks()
 
-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 demo:
+  gr.Markdown("<h1><center>Translate with Bloom</center></h1>")
+  
   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_lang = gr.Dropdown(LANGUAGES, value='English', label='Select input language')
+    output_lang = gr.Dropdown(LANGUAGES, value='French', label='Select output language')
  
-  input_prompt = gr.Textbox(label="Enter the sentence : ", 
-                            value=f"Instruction: ... \ninput: \"from sentence\" \n{to_lang} :",
-                            lines=6)
+  input_text = gr.Textbox(label="Input", lines=6)
   
-  generated_txt = gr.Textbox(lines=3)
+  output_text = gr.Textbox(lines=6, label="Output")
 
-  b1 = gr.Button("translate")
-  b1.click(translate,inputs=[ input_prompt, from_lang, to_lang], outputs=generated_txt) 
+  buton = gr.Button("translate")
+  buton.click(translate, inputs=[input_lang, output_lang, input_text], outputs=output_text) 
     
 demo.launch(enable_queue=True, debug=True)
-