Update app.py

app.py

@@ -1,141 +1,77 @@
 import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import os
-
-print("Loading naadini Telugu-Gemma...")
-
-token = os.environ.get("HF_TOKEN")
-
-tokenizer = AutoTokenizer.from_pretrained(
-    "google/gemma-2b-it",
-    token=token
-)
-
-base_model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2b-it",
-    torch_dtype=torch.float32,
-    device_map="cpu",
-    token=token
-)
-
-model = PeftModel.from_pretrained(
-    base_model,
-    "mohantvj/telugu-gemma",
-    token=token
-)
-
-print("Naadini loaded!")
-
 
 def generate_telugu(prompt, content_type):
-    system_prompts = {
-        "kavita (Poem)": "Write a beautiful Telugu poem about the given topic.",
-        "paata (Song)": "Write a Telugu song with pallavi and charanam.",
-        "katha (Story)": "Write a short Telugu story with moral.",
-        "padyam (Classical)": "Write a classical Telugu padyam.",
-        "sambashanana (Conversation)": "Reply naturally in Telugu.",
-    }
-
-    instruction = system_prompts.get(content_type, system_prompts["kavita (Poem)"])
-
-    full_prompt = f"""### Instruction:
-{instruction}
-
-### Input:
-{prompt}
-
-### Response:
-"""
-
-    inputs = tokenizer(full_prompt, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=200,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9,
-            repetition_penalty=1.3
-        )
-
-    response = tokenizer.decode(
-        outputs[0][inputs.input_ids.shape[1]:],
-        skip_special_tokens=True
-    )
-    return response
+    return f"Coming soon! నాదిని is being set up...\n\nYour prompt: {prompt}\nType: {content_type}\n\nPlease visit: https://huggingface.co/mohantvj/naadini-telugu for usage instructions."
 
-
-with gr.Blocks(title="Naadini - Telugu Creative AI") as demo:
+with gr.Blocks(title="నాదిని - Telugu Creative AI") as demo:
     gr.Markdown("""
-    # Naadini - Telugu Creative AI
-
-    Naadini means She who is the source of divine sound, poetry and wisdom
-
-    Note: Running on CPU. Generation takes 3-5 minutes. Please be patient!
-
-    Demo version uses Telugu-Gemma 2B.
-    For best quality use full Naadini model locally on Apple Silicon Mac.
-    Full model: https://huggingface.co/mohantvj/naadini-telugu
-
-    Telugu lo kavithalu, paatalu, kathalu raayandi!
+    # 🎵 నాదిని (Naadini) - Telugu Creative AI
+    **నాదిని** means *"She who is the source of divine sound, poetry and wisdom"*
+    
+    ⚠️ Full model requires Apple Silicon Mac. 
+    See [model page](https://huggingface.co/mohantvj/naadini-telugu) for installation.
+    
+    తెలుగులో కవితలు, పాటలు, కథలు రాయండి!
     """)
-
+    
     with gr.Row():
         with gr.Column():
             content_type = gr.Dropdown(
                 choices=[
-                    "kavita (Poem)",
-                    "paata (Song)",
-                    "katha (Story)",
-                    "padyam (Classical)",
-                    "sambashanana (Conversation)"
+                    "కవిత (Poem)",
+                    "పాట (Song)",
+                    "కథ (Story)",
+                    "పద్యం (Classical)",
+                    "సంభాషణ (Conversation)"
                 ],
-                value="kavita (Poem)",
+                value="కవిత (Poem)",
                 label="What to Write"
             )
             prompt = gr.Textbox(
-                placeholder="varsham gurinchi oka kavita raayandi...",
+                placeholder="వర్షం గురించి ఒక కవిత రాయండి...",
                 label="Your Prompt",
                 lines=3
             )
-            generate_btn = gr.Button(
-                "Generate / Raayandi",
-                variant="primary"
-            )
-
+            generate_btn = gr.Button("✨ Generate", variant="primary")
+        
         with gr.Column():
             output = gr.Textbox(
-                label="Naadini Response",
+                label="నాదిని's Response",
                 lines=15
             )
-
+    
     gr.Examples(
         examples=[
-            ["varsham gurinchi oka andhamaina kavita raayandi", "kavita (Poem)"],
-            ["amma gurinchi oka paata raayandi", "paata (Song)"],
-            ["pillala kosam oka chinna katha raayandi", "katha (Story)"],
-            ["vemana shaililo ahankaram gurinchi padyam raayandi", "padyam (Classical)"],
-            ["namaskaram! meeru ela unnaru?", "sambashanana (Conversation)"],
+            ["వర్షం గురించి ఒక అందమైన కవిత రాయండి", "కవిత (Poem)"],
+            ["అమ్మ గురించి ఒక పాట రాయండి", "పాట (Song)"],
+            ["పిల్లల కోసం ఒక చిన్న కథ ర��యండి", "కథ (Story)"],
         ],
         inputs=[prompt, content_type]
     )
-
+    
     generate_btn.click(
         fn=generate_telugu,
         inputs=[prompt, content_type],
         outputs=output
     )
-
+    
     gr.Markdown("""
     ---
-    Built with love by Thonangi Venkata Jagan Mohan
-
-    Full model: https://huggingface.co/mohantvj/naadini-telugu
-
-    For faster generation on Apple Silicon Mac install mlx-lm
+    Built with ❤️ by **Thonangi Venkata Jagan Mohan**
+    
+    ## How to Use Locally
+```bash
+    pip install mlx-lm
+```
+```python
+    from mlx_lm import load, generate
+    from mlx_lm.sample_utils import make_sampler
+    
+    model, tokenizer = load(
+        "mlx-community/Qwen3-14B-4bit",
+        adapter_path="mohantvj/naadini-telugu"
+    )
+```
     """)
 
 demo.launch()