Update app.py

app.py

@@ -1,28 +1,48 @@
-from huggingface_hub import InferenceApi
 import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
 
-# Note: Replace "your_hugging_face_api_key" with your actual API key.
-client = InferenceApi("mistralai/Mixtral-8x7B-Instruct-v0.1")
+# Specify the model name from the Hugging Face Hub, for example, an English to French model by the University of Helsinki
+model_name = "Helsinki-NLP/opus-mt-en-fr"
 
-def translate_text(input_text, target_language):
-    prompt = f"Translate the following text into {target_language}: {input_text}"
-    try:
-        # Adjusted to use a hypothetical 'generate' or similar method.
-        # You'll need to replace this with the actual method for sending inference requests.
-        response = client.generate(inputs=prompt)
-        # The response structure depends on the model and the API's current design.
-        # You may need to adjust how you extract the translated text from the response.
-        translated_text = response['generated_text'] if 'generated_text' in response else "Translation error or model response format has changed."
-    except Exception as e:
-        translated_text = f"Error: {str(e)}"
+# Load the tokenizer and model
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Function to handle translation
+def translate_text(text, target_language):
+    # Adjust the model_name based on the target language
+    # Note: You'd need to find the exact model names for each language pair you want to support
+    model_name_map = {
+        "French": "Helsinki-NLP/opus-mt-en-fr",
+        "German": "Helsinki-NLP/opus-mt-en-de",
+        "Spanish": "Helsinki-NLP/opus-mt-en-es",
+    }
+    
+    selected_model_name = model_name_map.get(target_language, "Helsinki-NLP/opus-mt-en-fr")
+    
+    # Load the selected model and tokenizer
+    tokenizer = MarianTokenizer.from_pretrained(selected_model_name)
+    model = MarianMTModel.from_pretrained(selected_model_name)
+    
+    # Prepare the text for translation
+    encoded_text = tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")
+    
+    # Perform the translation
+    translated = model.generate(**encoded_text)
+    
+    # Decode the translated text
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    
     return translated_text
 
+# Define the interface
 iface = gr.Interface(
     fn=translate_text,
-    inputs=[gr.Textbox(label="Text to Translate"), gr.Textbox(label="Target Language")],
-    outputs=gr.Textbox(label="Translated Text"),
-    title="Simple Translator with Mixtral",
-    description="Translate text to your specified language using the Mixtral model from Hugging Face."
+    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter text to translate..."), gr.inputs.Dropdown(["French", "German", "Spanish"], label="Select Language")],
+    outputs=[gr.outputs.Textbox()],
+    title="Text Translator with Helsinki NLP Models",
+    description="Select a language to translate English text into using University of Helsinki models."
 )
 
-iface.launch()
+# Launch the app
+iface.launch()