Update app.py

app.py

@@ -2,21 +2,21 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import torch
 
-model = AutoModelForSeq2SeqLM.from_pretrained("Mamadou2727/Feriji_Translator_v1.1")
+model = AutoModelForSeq2SeqLM.from_pretrained("Mamadou2727/m2m100_418M-fr")
 tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 LANG_CODES = {
-    "French":"fr",
-    "Zarma":"yo"
+    "French": "fr",
+    "Zarma": "yo"
 }
 
-def translate(text, src_lang, tgt_lang, candidates:int):
+def translate(text, candidates: int):
     """
-    Translate the text from source lang to target lang
+    Translate the text from French to Zarma
     """
 
-    src = LANG_CODES.get(src_lang)
-    tgt = LANG_CODES.get(tgt_lang)
+    src = LANG_CODES["French"]
+    tgt = LANG_CODES["Zarma"]
 
     tokenizer.src_lang = src
     tokenizer.tgt_lang = tgt
@@ -24,15 +24,14 @@ def translate(text, src_lang, tgt_lang, candidates:int):
     ins = tokenizer(text, return_tensors='pt').to(device)
 
     gen_args = {
-            'return_dict_in_generate': True,
-            'output_scores': True,
-            'output_hidden_states': True,
-            'length_penalty': 0.0,  # don't encourage longer or shorter output,
-            'num_return_sequences': candidates,
-            'num_beams':candidates,
-            'forced_bos_token_id': tokenizer.lang_code_to_id[tgt]
-        }
-    
+        'return_dict_in_generate': True,
+        'output_scores': True,
+        'output_hidden_states': True,
+        'length_penalty': 0.0,  # don't encourage longer or shorter output,
+        'num_return_sequences': candidates,
+        'num_beams': candidates,
+        'forced_bos_token_id': tokenizer.lang_code_to_id[tgt]
+    }
 
     outs = model.generate(**{**ins, **gen_args})
     output = tokenizer.batch_decode(outs.sequences, skip_special_tokens=True)
@@ -41,54 +40,41 @@ def translate(text, src_lang, tgt_lang, candidates:int):
 
 with gr.Blocks() as app:
     markdown = r"""
-    # Feriji-fr-to-dje v.1.1, Proudly made by Elysabhete, Habibatou & Mamadou K.
-    
-    ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63cc1d4bf488db9bb3c6449e/AtOKLAaL5kt0VhRsxE0vf.png)
-
-    Feriji-fr-to-dje is a beta version of the French to Zarma translator.
-        
-    ## Intended Uses & Limitations        
-    
-    This model is intended for academic research and practical applications in machine translation. It can be used to translate French text to Zarma and vice versa. Users should note that the model's performance may vary based on the complexity and context of the input text.
-        
-    ## Authors:
-    The project, **Feriji dataset and Feriji-fr-to-dje**, was curated by **Elysabhete Ibrahim Amadou** and **Mamadou K. KEITA**, with the aim to enhance linguistic studies and translation capabilities between French and Zarma.
+        # Feriji-fr-to-dje v.1.1, Proudly made by Elysabhete, Habibatou & Mamadou K.
         
-    ## Citations
+        <img src="https://cdn-uploads.huggingface.co/production/uploads/63cc1d4bf488db9bb3c6449e/AtOKLAaL5kt0VhRsxE0vf.png" width="300" height="250">
         
-    If you use this dataset or model in your research, please cite it as follows:
+        Feriji-fr-to-dje is a beta version of the French to Zarma translator.
+            
+        ## Intended Uses & Limitations        
         
-    @dataset{Feriji,
-      author       = {Habibatou Abdoulaye Alfari, Elysabhete Ibrahim Amadou and Mamadou K. KEITA},
-      title        = {Feriji, a French-Zarma Parallel Corpus},
-      year         = 2023,
-      publisher    = {GitHub},
-      journal      = {GitHub repository},
-      howpublished = {\url{https://github.com/27-GROUP/Feriji}}
-    }
+        This model is intended for academic research and practical applications in machine translation. It can be used to translate French text to Zarma and vice versa. Users should note that the model's performance may vary based on the complexity and context of the input text.
+            
+        ## Authors:
+        The project, **Feriji dataset and Feriji-fr-to-dje**, was curated by **Elysabhete Ibrahim Amadou** and **Mamadou K. KEITA**, with the aim to enhance linguistic studies and translation capabilities between French and Zarma.
+            
+        ## Citations
+            
+        If you use this dataset or model in your research, please cite it as follows:
+            
+        @dataset{Feriji,
+          author       = {Habibatou Abdoulaye Alfari, Elysabhete Ibrahim Amadou and Mamadou K. KEITA},
+          title        = {Feriji, a French-Zarma Parallel Corpus},
+          year         = 2023,
+          publisher    = {GitHub},
+          journal      = {GitHub repository},
+          howpublished = {\url{https://github.com/27-GROUP/Feriji}}
+        }
     """
 
-
     with gr.Row():
         gr.Markdown(markdown)
         with gr.Column():
             input_text = gr.components.Textbox(label="Input Text", value="")
             return_seqs = gr.Slider(label="Number of return sequences", value=1, minimum=1, maximum=12, step=1)
-            source_lang = gr.components.Dropdown(label="Source Language", value="French", choices=list(LANG_CODES.keys()))
-            target_lang = gr.components.Dropdown(label="Target Language", value="Zarma", choices=list(LANG_CODES.keys()))            
-            inputs=[input_text, source_lang, target_lang, return_seqs]
-            outputs = gr.Textbox()
+            outputs = gr.Textbox(label="Output Text")
 
             translate_btn = gr.Button("Traduis!")
-            translate_btn.click(translate, inputs=inputs, outputs=outputs)
-""""
-            gr.Examples(
-                [
-                    ["Hello! How are you?", "English", "toki pona", 3],
-                    ["toki a! ilo pi ante toki ni li pona!", "toki pona", "English",  3],
-                    ["mi li toki e toki pona", "toki pona", "toki pona", 3],
-                ],
-                inputs=inputs
-            )
-"""
+            translate_btn.click(translate, inputs=[input_text, return_seqs], outputs=outputs)
+
 app.launch(share=True)