app.py

app.py

@@ -1,69 +1,181 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
+import torch
 
+# ----------------------------------------
+# Supported Languages
+# ----------------------------------------
+languages = [
+    "English",
+    "Hindi",
+    "Tamil",
+    "Telugu",
+    "Kannada",
+    "Malayalam",
+    "Bengali",
+    "Marathi",
+    "Gujarati",
+    "Punjabi",
+    "Urdu"
+]
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+# ----------------------------------------
+# Translation Function
+# ----------------------------------------
+def translate_text(hf_token, source_lang, target_lang, input_text):
 
-    messages = [{"role": "system", "content": system_message}]
+    if not hf_token.strip():
+        return "Please enter Hugging Face Access Token."
 
-    messages.extend(history)
+    if not input_text.strip():
+        return "Please enter text."
 
-    messages.append({"role": "user", "content": message})
+    try:
 
-    response = ""
+        # Load IBM Granite model
+        pipe = pipeline(
+            "text-generation",
+            model="ibm-granite/granite-3.3-2b-base",
+            token=hf_token,
+            device_map="auto",
+            torch_dtype=torch.float16
+        )
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+        # Translation Prompt
+        prompt = f"""
+You are an expert multilingual translator.
 
-        response += token
-        yield response
+Translate the below text from {source_lang} to {target_lang}.
 
+Only provide translated text.
 
+Text:
+{input_text}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+
+        # Generate Translation
+        result = pipe(
+            prompt,
+            max_new_tokens=200,
+            temperature=0.2
+        )
+
+        translated_text = result[0]["generated_text"]
+
+        # Remove original prompt if generated
+        translated_text = translated_text.replace(prompt, "").strip()
+
+        return translated_text
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# ----------------------------------------
+# Professional UI Styling
+# ----------------------------------------
+custom_css = """
+body {
+    background: linear-gradient(to right, #0f2027, #203a43, #2c5364);
+}
+
+.gradio-container {
+    font-family: 'Segoe UI', sans-serif;
+}
+
+.main-title {
+    text-align: center;
+    font-size: 42px;
+    font-weight: bold;
+    color: white;
+}
+
+.sub-title {
+    text-align: center;
+    color: #dfe6e9;
+    font-size: 18px;
+    margin-bottom: 20px;
+}
+
+textarea {
+    border-radius: 12px !important;
+}
+
+footer {
+    visibility: hidden;
+}
 """
-chatbot = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+# ----------------------------------------
+# Gradio Interface
+# ----------------------------------------
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css=custom_css
+) as demo:
+
+    gr.Markdown(
+        """
+        <div class="main-title">
+            🌍 AI Multilingual Translator
+        </div>
+
+        <div class="sub-title">
+            IBM Granite + Hugging Face + Gradio
+        </div>
+        """
+    )
+
+    with gr.Row():
+
+        with gr.Column(scale=1):
+
+            hf_token = gr.Textbox(
+                label="🔑 Hugging Face Access Token",
+                placeholder="Paste your HF token here",
+                type="password"
+            )
+
+            source_lang = gr.Dropdown(
+                choices=languages,
+                value="English",
+                label="🌐 Source Language"
+            )
+
+            target_lang = gr.Dropdown(
+                choices=languages,
+                value="Hindi",
+                label="🎯 Target Language"
+            )
+
+        with gr.Column(scale=2):
+
+            input_text = gr.Textbox(
+                label="📝 Enter Text",
+                lines=8,
+                placeholder="Type text to translate..."
+            )
+
+            output_text = gr.Textbox(
+                label="✅ Translated Output",
+                lines=8
+            )
+
+    translate_btn = gr.Button(
+        "🚀 Translate",
+        variant="primary"
+    )
+
+    translate_btn.click(
+        fn=translate_text,
+        inputs=[
+            hf_token,
+            source_lang,
+            target_lang,
+            input_text
+        ],
+        outputs=output_text
+    )
+
+# Launch App
+demo.launch(share=True)