//

.gradio/flagged/dataset3.csv

@@ -0,0 +1,2 @@
+Input Text,Translation,timestamp
+Thobela,,2025-07-27 21:39:41.511509

app.py

@@ -1,40 +1,40 @@
-# import requests
-# import gradio as gr
-# from dotenv import load_dotenv
-# import os
+import requests
+import gradio as gr
+from dotenv import load_dotenv
+import os
 
-# # Load environment variables from .env file
-# load_dotenv()
-# HF_TOKEN = os.getenv("HF_TOKEN")
+# Load environment variables from .env file
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# model_name = "Helsinki-NLP/opus-mt-en-nso"
-# API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
-# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+model_name = "Helsinki-NLP/opus-mt-en-nso"
+API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
 
-# def query(payload):
-#     # HTTP POST Request
-#     response = requests.post(API_URL, headers=headers, json=payload)
-#     return response.json()
+def query(payload):
+    # HTTP POST Request
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 
-# def translate(input_text):
-#     # API Request:
-#     response = query({"inputs": input_text, "options": {"wait_for_model": True}})
+def translate(input_text):
+    # API Request:
+    response = query({"inputs": input_text, "options": {"wait_for_model": True}})
 
-#     translation = response[0]["translation_text"]
+    translation = response[0]["translation_text"]
 
-#     return translation
+    return translation
 
 
-# translator = gr.Interface(
-#     fn=translate,
-#     inputs=[gr.Textbox(label="Input Text", placeholder="Input Text To Be Translated")],
-#     outputs=gr.Textbox(label="Translation"),
-#     title="Translademia",
-# )
+translator = gr.Interface(
+    fn=translate,
+    inputs=[gr.Textbox(label="Input Text", placeholder="Input Text To Be Translated")],
+    outputs=gr.Textbox(label="Translation"),
+    title="Translademia",
+)
 
-# translator.launch()
+translator.launch(share=True)
 
 
 # import requests
@@ -182,79 +182,79 @@
 # ///////////////////////////////////////////////////////////////////////////////////////////////////////////////
 # Using Unesco API
 
-import requests
-import gradio as gr
-from dotenv import load_dotenv
-import os
+# import requests
+# import gradio as gr
+# from dotenv import load_dotenv
+# import os
 
-# Load Hugging Face token from .env
-load_dotenv()
-HF_TOKEN = os.getenv("HF_TOKEN")
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+# # Load Hugging Face token from .env
+# load_dotenv()
+# HF_TOKEN = os.getenv("HF_TOKEN")
+# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
-# NLLB model endpoint
-MODEL_NAME = "facebook/nllb-200-3.3B"
-API_URL = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"
-
-# Define supported language pairs and NLLB codes
-LANGUAGE_PAIRS = {
-    "English → Afrikaans": ("eng_Latn", "afr_Latn"),
-    "English → Xhosa": ("eng_Latn", "xho_Latn"),
-    "English → Zulu": ("eng_Latn", "zul_Latn"),
-    "English → Sesotho": ("eng_Latn", "sot_Latn"),
-    "English → Tswana": ("eng_Latn", "tsn_Latn"),
-    "English → Northern Sotho": ("eng_Latn", "nso_Latn"),
-    "English → Swati": ("eng_Latn", "ssw_Latn"),
-    "English → Tsonga": ("eng_Latn", "tso_Latn"),
-    "Afrikaans → English": ("afr_Latn", "eng_Latn"),
-    "Xhosa → English": ("xho_Latn", "eng_Latn"),
-    "Zulu → English": ("zul_Latn", "eng_Latn"),
-    "Sesotho → English": ("sot_Latn", "eng_Latn"),
-    "Tswana → English": ("tsn_Latn", "eng_Latn"),
-    "Northern Sotho → English": ("nso_Latn", "eng_Latn"),
-    "Swati → English": ("ssw_Latn", "eng_Latn"),
-    "Tsonga → English": ("tso_Latn", "eng_Latn"),
-}
-
-
-def translate(input_text, language_pair):
-    if not input_text.strip():
-        return "[ERROR] Please enter some text to translate."
-
-    # Get source and target language codes
-    src_lang, tgt_lang = LANGUAGE_PAIRS[language_pair]
-
-    # Prepend target language token to the input
-    formatted_input = f">>{tgt_lang}<< {input_text.strip()}"
-
-    # Send request to Hugging Face Inference API
-    payload = {
-        "inputs": formatted_input,
-        "options": {"wait_for_model": True},
-    }
+# # NLLB model endpoint
+# MODEL_NAME = "facebook/nllb-200-3.3B"
+# API_URL = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"
 
-    response = requests.post(API_URL, headers=headers, json=payload)
+# # Define supported language pairs and NLLB codes
+# LANGUAGE_PAIRS = {
+#     "English → Afrikaans": ("eng_Latn", "afr_Latn"),
+#     "English → Xhosa": ("eng_Latn", "xho_Latn"),
+#     "English → Zulu": ("eng_Latn", "zul_Latn"),
+#     "English → Sesotho": ("eng_Latn", "sot_Latn"),
+#     "English → Tswana": ("eng_Latn", "tsn_Latn"),
+#     "English → Northern Sotho": ("eng_Latn", "nso_Latn"),
+#     "English → Swati": ("eng_Latn", "ssw_Latn"),
+#     "English → Tsonga": ("eng_Latn", "tso_Latn"),
+#     "Afrikaans → English": ("afr_Latn", "eng_Latn"),
+#     "Xhosa → English": ("xho_Latn", "eng_Latn"),
+#     "Zulu → English": ("zul_Latn", "eng_Latn"),
+#     "Sesotho → English": ("sot_Latn", "eng_Latn"),
+#     "Tswana → English": ("tsn_Latn", "eng_Latn"),
+#     "Northern Sotho → English": ("nso_Latn", "eng_Latn"),
+#     "Swati → English": ("ssw_Latn", "eng_Latn"),
+#     "Tsonga → English": ("tso_Latn", "eng_Latn"),
+# }
 
-    if response.status_code != 200:
-        return f"[ERROR] {response.status_code}: {response.text}"
 
-    try:
-        output = response.json()
-        return output[0]["translation_text"]
-    except Exception as e:
-        return f"[ERROR] Failed to parse response: {e}"
+# def translate(input_text, language_pair):
+#     if not input_text.strip():
+#         return "[ERROR] Please enter some text to translate."
 
+#     # Get source and target language codes
+#     src_lang, tgt_lang = LANGUAGE_PAIRS[language_pair]
 
-# Gradio UI
-translator = gr.Interface(
-    fn=translate,
-    inputs=[
-        gr.Textbox(label="Input Text", placeholder="Type text here..."),
-        gr.Dropdown(choices=list(LANGUAGE_PAIRS.keys()), label="Select Language Pair"),
-    ],
-    outputs=gr.Textbox(label="Translation"),
-    title="Translademia (NLLB Edition)",
-    description="Translate between English and South African languages using Meta's NLLB-200 multilingual model.",
-)
+#     # Prepend target language token to the input
+#     formatted_input = f">>{tgt_lang}<< {input_text.strip()}"
 
-translator.launch(share=True)
+#     # Send request to Hugging Face Inference API
+#     payload = {
+#         "inputs": formatted_input,
+#         "options": {"wait_for_model": True},
+#     }
+
+#     response = requests.post(API_URL, headers=headers, json=payload)
+
+#     if response.status_code != 200:
+#         return f"[ERROR] {response.status_code}: {response.text}"
+
+#     try:
+#         output = response.json()
+#         return output[0]["translation_text"]
+#     except Exception as e:
+#         return f"[ERROR] Failed to parse response: {e}"
+
+
+# # Gradio UI
+# translator = gr.Interface(
+#     fn=translate,
+#     inputs=[
+#         gr.Textbox(label="Input Text", placeholder="Type text here..."),
+#         gr.Dropdown(choices=list(LANGUAGE_PAIRS.keys()), label="Select Language Pair"),
+#     ],
+#     outputs=gr.Textbox(label="Translation"),
+#     title="Translademia (NLLB Edition)",
+#     description="Translate between English and South African languages using Meta's NLLB-200 multilingual model.",
+# )
+
+# translator.launch(share=True)

main.py

@@ -1,11 +1,10 @@
-import requests
+from transformers import pipeline
 
-API_URL = "https://api-inference.huggingface.co/models/facebook/nllb-200-3.3B"
-HF_TOKEN = ""
+# Create translation pipeline
+translator = pipeline("translation", model="facebook/nllb-200-3.3B")
 
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-payload = {"inputs": ">>zul_Latn<< Hello, how are you?"}
+# Translate English to Zulu (you prepend the target language code in input)
+input_text = ">>zul_Latn<< Hello, how are you?"
 
-response = requests.post(API_URL, headers=headers, json=payload)
-print(response.status_code)
-print(response.text)
+result = translator(input_text)
+print(result[0]["translation_text"])

tempCodeRunnerFile.py

@@ -1,37 +0,0 @@
-import requests
-import gradio as gr
-from dotenv import load_dotenv
-import os
-
-# Load environment variables from .env file
-load_dotenv()
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-model_name = "Helsinki-NLP/opus-mt-en-nso"
-API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-
-def query(payload):
-    # HTTP POST Request
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-
-def translate(input_text):
-    # API Request:
-    response = query({"inputs": input_text, "options": {"wait_for_model": True}})
-
-    translation = response[0]["translation_text"]
-
-    return translation
-
-
-translator = gr.Interface(
-    fn=translate,
-    inputs=[gr.Textbox(label="Input Text", placeholder="Input Text To Be Translated")],
-    outputs=gr.Textbox(label="Translation"),
-    title="Translademia",
-)
-
-translator.launch()

two.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+import torch
+
+# Load NLLB-200 model and tokenizer
+model_name = "facebook/nllb-200-3.3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Define supported language pairs and NLLB codes
+LANGUAGE_PAIRS = {
+    "English → Afrikaans": ("eng_Latn", "afr_Latn"),
+    "English → Xhosa": ("eng_Latn", "xho_Latn"),
+    "English → Zulu": ("eng_Latn", "zul_Latn"),
+    "English → Sesotho": ("eng_Latn", "sot_Latn"),
+    "English → Tswana": ("eng_Latn", "tsn_Latn"),
+    "English → Northern Sotho": ("eng_Latn", "nso_Latn"),
+    "English → Swati": ("eng_Latn", "ssw_Latn"),
+    "English → Tsonga": ("eng_Latn", "tso_Latn"),
+    "Afrikaans → English": ("afr_Latn", "eng_Latn"),
+    "Xhosa → English": ("xho_Latn", "eng_Latn"),
+    "Zulu → English": ("zul_Latn", "eng_Latn"),
+    "Sesotho → English": ("sot_Latn", "eng_Latn"),
+    "Tswana → English": ("tsn_Latn", "eng_Latn"),
+    "Northern Sotho → English": ("nso_Latn", "eng_Latn"),
+    "Swati → English": ("ssw_Latn", "eng_Latn"),
+    "Tsonga → English": ("tso_Latn", "eng_Latn"),
+}
+
+
+def translate(input_text, language_pair):
+    if not input_text.strip():
+        return "[ERROR] Please enter text."
+
+    _, tgt_lang = LANGUAGE_PAIRS[language_pair]
+
+    # Prepend target language token
+    input_with_lang = f">>{tgt_lang}<< {input_text.strip()}"
+
+    # Tokenize and generate
+    inputs = tokenizer(input_with_lang, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_length=256)
+
+    translated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translated
+
+
+# Gradio Interface
+translator = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Input Text", placeholder="Type text here..."),
+        gr.Dropdown(choices=list(LANGUAGE_PAIRS.keys()), label="Select Language Pair"),
+    ],
+    outputs=gr.Textbox(label="Translation"),
+    title="Translademia (Local NLLB Edition)",
+    description="Translate between English and South African languages using Meta's NLLB-200 locally.",
+)
+
+translator.launch(share=True)