Update app.py

app.py

@@ -4,15 +4,59 @@ import streamlit as st
 import requests
 from transformers import pipeline
 from PIL import Image
-from googletrans import Translator
 import sqlite3
 
 # Initialize Streamlit app
 st.set_page_config(page_title="Image Caption Generator", layout="wide")
 
-translator = Translator()
+
 
 @st.cache_resource
+
+##Bloom
+API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
+# HF_TOKEN = os.environ["HF_TOKEN"]
+# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def translate(prompt_ , to_lang): 
+  input_prompt = "translate this"
+  seed = 42
+  prompt =  f"Instruction : Given an English 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
+
+
 # Image to Text
 def image_to_text(url):
     # Load a transformer
@@ -101,9 +145,7 @@ def login_section():
         except sqlite3.OperationalError as e:
             st.error(f"An error occurred while trying to log in: {e}")
 
-def translate_caption(caption, target_language="en"):
-    translated = translator.translate(caption, dest=target_language)
-    return translated.text
+
 
 def predict(cap_col, target_language):
     captions = []
@@ -111,13 +153,13 @@ def predict(cap_col, target_language):
     pred_caption = image_to_text(url)
 
     cap_col.markdown('#### Predicted Captions:')
-    translated_caption = translate_caption(pred_caption, target_language)
+    translated_caption = translate(pred_caption, target_language)
     captions.append(translated_caption)
 
     for _ in range(4):
         pred_caption = image_to_text(url)
         if pred_caption not in captions:
-            translated_caption = translate_caption(pred_caption, target_language)
+            translated_caption = translate(pred_caption, target_language)
             captions.append(translated_caption)
 
     cap_col.markdown('<div class="caption-container">', unsafe_allow_html=True)
@@ -136,7 +178,9 @@ def generate_caption_section():
     img_upload = st.file_uploader(label='Upload Image', type=['jpg', 'png', 'jpeg'])
 
     # Language selection dropdown
-    target_language = st.selectbox('Select Target Language', ['en', 'ta', 'hi', 'es', 'fr', 'zh-cn', 'ko'], index=0)
+           
+                          
+    target_language = st.selectbox('Select Target Language', ['English', 'Spanish', 'Hindi', 'Italian'], index=0)
 
     # Process image and generate captions
     if img_url: