trial

app.py

@@ -2,14 +2,66 @@ import torch
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from IndicTransToolkit.processor import IndicProcessor
 import gradio as gr
+import requests
+from datetime import datetime
 
+# Supabase configuration
+SUPABASE_URL = "https://gptmdbhzblfybdnohqnh.supabase.co"  # ← Replace with your project URL
+SUPABASE_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImdwdG1kYmh6YmxmeWJkbm9ocW5oIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDc0NjY1NDgsImV4cCI6MjA2MzA0MjU0OH0.CfWArts6Kd_x7Wj0a_nAyGJfrFt8F7Wdy_MdYDj9e7U"                 # ← Replace with your anon/public API key
+SUPABASE_TABLE = "translations"
+
+# Device configuration
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Load model and tokenizer
 model_name = "ai4bharat/indictrans2-en-indic-1B"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=True).to(DEVICE)
 ip = IndicProcessor(inference=True)
 
+# Function to save translation to Supabase
+def save_to_supabase(input_text, output_text):
+    payload = {
+        "timestamp": datetime.utcnow().isoformat(),
+        "input_text": input_text,
+        "output_text": output_text
+    }
+
+    headers = {
+        "apikey": SUPABASE_API_KEY,
+        "Authorization": f"Bearer {SUPABASE_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    response = requests.post(
+        f"{SUPABASE_URL}/rest/v1/{SUPABASE_TABLE}",
+        headers=headers,
+        json=payload
+    )
+
+    return response.status_code == 201
+
+# Function to retrieve recent translation history from Supabase
+def get_translation_history():
+    headers = {
+        "apikey": SUPABASE_API_KEY,
+        "Authorization": f"Bearer {SUPABASE_API_KEY}"
+    }
+
+    response = requests.get(
+        f"{SUPABASE_URL}/rest/v1/{SUPABASE_TABLE}?order=timestamp.desc&limit=10",
+        headers=headers
+    )
+
+    if response.status_code == 200:
+        records = response.json()
+        return "\n\n".join(
+            [f"[{r['timestamp']}]\nEN: {r['input_text']}\nKS: {r['output_text']}" for r in records]
+        )
+    else:
+        return "Failed to load history."
+
+# Translation function
 def translate_english_to_kashmiri(text):
     if not text.strip():
         return "Please enter some English text."
@@ -30,15 +82,25 @@ def translate_english_to_kashmiri(text):
 
     translated = tokenizer.batch_decode(outputs, skip_special_tokens=True)
     result = ip.postprocess_batch(translated, lang=tgt_lang)[0]
+
+    # Save to Supabase
+    save_to_supabase(text, result)
+
     return result
 
-interface = gr.Interface(
-    fn=translate_english_to_kashmiri,
-    inputs=gr.Textbox(lines=2, label="English Text", placeholder="Enter English text..."),
-    outputs=gr.Textbox(lines=2, label="Kashmiri Translation", placeholder="Translated Kashmiri text..."),
-    title="English to Kashmiri Translator",
-    description="IndicTrans2 model"
-)
+# Gradio Interface
+with gr.Blocks() as interface:
+    gr.Markdown("## English to Kashmiri Translator\nIndicTrans2 model with Supabase history logging.")
+
+    with gr.Row():
+        input_text = gr.Textbox(lines=2, label="English Text", placeholder="Enter English text...")
+        output_text = gr.Textbox(lines=2, label="Kashmiri Translation", placeholder="Translated Kashmiri text...")
+
+    translate_button = gr.Button("Translate")
+    history_box = gr.Textbox(lines=10, label="Translation History", interactive=False)
+
+    translate_button.click(fn=translate_english_to_kashmiri, inputs=input_text, outputs=output_text)\
+                    .then(fn=get_translation_history, outputs=history_box)
 
 if __name__ == "__main__":
-    interface.launch(share=True)
+    interface.launch()

requirements.txt

@@ -4,4 +4,5 @@ sentencepiece
 nltk
 mosestokenizer
 gradio
+requests
 git+https://github.com/VarunGumma/IndicTransToolkit.git