Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+import subprocess
+import sys
+import os
+
+# --- Configuration ---
+SYLHETI_TO_BN_MODEL = "shbhro/sylhetit5"
+BN_TO_EN_MODEL = "csebuetnlp/banglat5_nmt_bn_en"
+NORMALIZER_REPO = "https://github.com/csebuetnlp/normalizer.git"
+
+# --- Helper function to install/import normalizer ---
+# This ensures the normalizer is available.
+# In HF Spaces, requirements.txt is the primary method.
+normalizer_module = None
+try:
+    from normalizer import normalize as normalize_fn_imported
+    normalizer_module = normalize_fn_imported
+    print("Normalizer imported successfully.")
+except ImportError:
+    print(f"Normalizer library not found. Attempting to install from {NORMALIZER_REPO}...")
+    try:
+        # This command installs the package directly from git.
+        # The #egg=normalizer part helps pip identify the package name.
+        subprocess.check_call([sys.executable, "-m", "pip", "install", f"git+{NORMALIZER_REPO}#egg=normalizer"])
+        from normalizer import normalize as normalize_fn_imported_after_install
+        normalizer_module = normalize_fn_imported_after_install
+        print("Normalizer installed and imported successfully after pip install.")
+    except Exception as e:
+        print(f"Failed to install or import normalizer: {e}")
+        print("Please ensure 'git+https://github.com/csebuetnlp/normalizer.git#egg=normalizer' is in your requirements.txt for Hugging Face Spaces.")
+        # Fallback to a dummy function if installation fails, so the app can still load and show an error.
+        def dummy_normalize(text):
+            raise RuntimeError("Normalizer library could not be loaded. Please check installation.")
+        normalizer_module = dummy_normalize
+
+# --- Model Loading (Globally, when the script starts) ---
+sylheti_to_bn_pipe = None
+bn_to_en_model = None
+bn_to_en_tokenizer = None
+model_device = None
+
+print("Loading translation models...")
+try:
+    model_device_type = "cuda" if torch.cuda.is_available() else "cpu"
+    model_device = torch.device(model_device_type)
+    hf_device_param = 0 if model_device_type == "cuda" else -1 # For pipeline
+
+    print(f"Using device: {model_device_type}")
+
+    sylheti_to_bn_pipe = pipeline(
+        "text2text-generation",
+        model=SYLHETI_TO_BN_MODEL,
+        device=hf_device_param
+    )
+    print(f"Sylheti-to-Bengali model ({SYLHETI_TO_BN_MODEL}) loaded.")
+
+    bn_to_en_model = AutoModelForSeq2SeqLM.from_pretrained(BN_TO_EN_MODEL)
+    bn_to_en_tokenizer = AutoTokenizer.from_pretrained(BN_TO_EN_MODEL, use_fast=False)
+    bn_to_en_model.to(model_device)
+    print(f"Bengali-to-English model ({BN_TO_EN_MODEL}) loaded.")
+
+except Exception as e:
+    print(f"FATAL: Error loading one or more models: {e}")
+    # To prevent the app from crashing entirely if models don't load,
+    # but it will show errors during translation.
+    sylheti_to_bn_pipe = None
+    bn_to_en_model = None
+    bn_to_en_tokenizer = None
+
+# --- Main Translation Logic ---
+def translate_sylheti_to_english_gradio(sylheti_text_input):
+    if not sylheti_text_input.strip():
+        return "Please enter some Sylheti text.", ""
+
+    if not sylheti_to_bn_pipe:
+        return "Error: Sylheti-to-Bengali model not loaded. Check logs.", ""
+    if not bn_to_en_model or not bn_to_en_tokenizer:
+        return "Error: Bengali-to-English model not loaded. Check logs.", ""
+    if normalizer_module is None or isinstance(normalizer_module, type(lambda:0)) and normalizer_module.__name__ == 'dummy_normalize': # Check if it's the dummy
+        return "Error: Bengali normalizer library not available. Check logs.", ""
+
+
+    bengali_text_intermediate = "Error in Sylheti to Bengali step."
+    english_text_final = "Error in Bengali to English step."
+
+    # Step 1: Sylheti → Bengali
+    try:
+        print(f"Translating Sylheti to Bengali: '{sylheti_text_input}'")
+        bengali_translation_outputs = sylheti_to_bn_pipe(
+            sylheti_text_input,
+            max_length=128,
+            num_beams=5,
+            early_stopping=True
+        )
+        bengali_text_intermediate = bengali_translation_outputs[0]['generated_text']
+        print(f"Intermediate Bengali: '{bengali_text_intermediate}'")
+    except Exception as e:
+        print(f"Error during Sylheti to Bengali translation: {e}")
+        bengali_text_intermediate = f"Sylheti->Bengali Error: {str(e)}"
+        return bengali_text_intermediate, english_text_final # Stop if first step fails
+
+    # Step 2: Bengali → English
+    try:
+        print(f"Normalizing and translating Bengali to English: '{bengali_text_intermediate}'")
+        normalized_bn_text = normalizer_module(bengali_text_intermediate)
+        print(f"Normalized Bengali: '{normalized_bn_text}'")
+
+        input_ids = bn_to_en_tokenizer(
+            normalized_bn_text,
+            return_tensors="pt"
+        ).input_ids.to(model_device) # Ensure tensor is on the same device
+
+        generated_tokens = bn_to_en_model.generate(
+            input_ids,
+            max_length=128,
+            num_beams=5,
+            early_stopping=True
+        )
+        english_text_list = bn_to_en_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+        english_text_final = english_text_list[0] if english_text_list else "No English output generated."
+        print(f"Final English: '{english_text_final}'")
+    except Exception as e:
+        print(f"Error during Bengali to English translation: {e}")
+        english_text_final = f"Bengali->English Error: {str(e)}"
+
+    return bengali_text_intermediate, english_text_final
+
+# --- Gradio Interface Definition ---
+iface = gr.Interface(
+    fn=translate_sylheti_to_english_gradio,
+    inputs=gr.Textbox(
+        lines=4,
+        label="Enter Sylheti Text",
+        placeholder="কিতা কিতা কিনলায় তে?"
+    ),
+    outputs=[
+        gr.Textbox(label="Intermediate Bengali Output", lines=4),
+        gr.Textbox(label="Final English Output", lines=4)
+    ],
+    title="🌍 Sylheti to English Translator (via Bengali)",
+    description=(
+        "Translates Sylheti text to English in two steps:\n"
+        f"1. Sylheti → Bengali (using `{SYLHETI_TO_BN_MODEL}`)\n"
+        f"2. Bengali → English (using `{BN_TO_EN_MODEL}` with text normalization from `{NORMALIZER_REPO.split('/')[-1]}`)"
+    ),
+    examples=[
+        ["কিতা কিতা কিনলায় তে?"],
+        ["তুমি কিতা কররায়?"],
+        ["আমি ভাত খাইছি।"],
+        ["আফনে ভালা আছনি?"]
+    ],
+    allow_flagging="never",
+    theme=gr.themes.Soft() # Optional: adds a bit of styling
+)
+
+# --- Launch the Gradio app ---
+if __name__ == "__main__":
+    # When running locally, this launches the server.
+    # In Hugging Face Spaces, the `app.py` is typically run by their infrastructure.
+    iface.launch()