Update app.py

app.py

@@ -2,10 +2,14 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel, PeftConfig
 import torch
+import os
+
+# Get token from Space secrets
+HF_TOKEN = os.environ.get("HF_TOKEN")
 
 # Your model details
 PEFT_MODEL_ID = "Reubencf/gemma3-goan-finetuned"
-BASE_MODEL_ID = "google/gemma-2-2b-it"  # Base model used for fine-tuning
+BASE_MODEL_ID = "google/gemma-3-4b-it"  # Correct base model
 
 # UI Configuration
 TITLE = "🌴 Gemma Goan Q&A Bot"
@@ -14,7 +18,6 @@ This is a Gemma-2-2B model fine-tuned on Goan Q&A dataset using LoRA.
 Ask questions about Goa, Konkani culture, or general topics!
 
 **Model**: [Reubencf/gemma3-goan-finetuned](https://huggingface.co/Reubencf/gemma3-goan-finetuned)
-**Base Model**: google/gemma-2-2b-it
 """
 
 print("Loading model... This might take a few minutes on first run.")
@@ -23,17 +26,21 @@ try:
     # Load LoRA config to check base model
     peft_config = PeftConfig.from_pretrained(PEFT_MODEL_ID)
     
-    # Load base model
+    # Load base model WITH TOKEN
     print(f"Loading base model: {BASE_MODEL_ID}")
     base_model = AutoModelForCausalLM.from_pretrained(
         BASE_MODEL_ID,
+        token=HF_TOKEN,  # Add token here
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
         device_map="auto",
         low_cpu_mem_usage=True,
     )
     
-    # Load tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)
+    # Load tokenizer WITH TOKEN
+    tokenizer = AutoTokenizer.from_pretrained(
+        BASE_MODEL_ID,
+        token=HF_TOKEN  # Add token here
+    )
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token
         tokenizer.padding_side = "right"
@@ -58,11 +65,15 @@ except Exception as e:
     from peft import AutoPeftModelForCausalLM
     model = AutoPeftModelForCausalLM.from_pretrained(
         PEFT_MODEL_ID,
+        token=HF_TOKEN,  # Add token here
         device_map="auto",
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
         low_cpu_mem_usage=True,
     )
-    tokenizer = AutoTokenizer.from_pretrained(PEFT_MODEL_ID)
+    tokenizer = AutoTokenizer.from_pretrained(
+        PEFT_MODEL_ID,
+        token=HF_TOKEN  # Add token here
+    )
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token
 
@@ -78,14 +89,12 @@ def generate_response(
     
     # Format the prompt using Gemma chat template
     if history:
-        # Build conversation history
         conversation = ""
         for user, assistant in history:
             conversation += f"<start_of_turn>user\n{user}<end_of_turn>\n"
             conversation += f"<start_of_turn>model\n{assistant}<end_of_turn>\n"
         conversation += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
     else:
-        # Single turn conversation
         conversation = f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
     
     # Tokenize
@@ -128,113 +137,26 @@ def generate_response(
 
 # Example questions
 examples = [
-    ["What is Bebinca?"],
-    ["who is promod sawant?"],
+    ["What is the capital of Goa?"],
+    ["Tell me about Konkani language"],
+    ["What are famous beaches in Goa?"],
+    ["What is Goan fish curry?"],
     ["Explain the history of Old Goa"],
-    ["What are some popular festivals in Goa?"],
 ]
 
-# Custom CSS for better appearance
-custom_css = """
-#component-0 {
-    max-width: 900px;
-    margin: auto;
-}
-.gradio-container {
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-}
-"""
-
-# Create Gradio Chat Interface
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown(f"# {TITLE}")
-    gr.Markdown(DESCRIPTION)
-    
-    chatbot = gr.Chatbot(
-        height=450,
-        show_label=False,
-        avatar_images=(None, "🤖"),
-    )
-    
-    msg = gr.Textbox(
-        label="Ask a question",
-        placeholder="Type your question about Goa, Konkani culture, or any topic...",
-        lines=2,
-    )
-    
-    with gr.Accordion("⚙️ Generation Settings", open=False):
-        temperature = gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.7,
-            step=0.1,
-            label="Temperature (Creativity)",
-            info="Higher = more creative, Lower = more focused"
-        )
-        max_tokens = gr.Slider(
-            minimum=50,
-            maximum=512,
-            value=256,
-            step=10,
-            label="Max New Tokens",
-            info="Maximum length of the response"
-        )
-        top_p = gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (Nucleus Sampling)",
-        )
-        rep_penalty = gr.Slider(
-            minimum=1.0,
-            maximum=2.0,
-            value=1.1,
-            step=0.1,
-            label="Repetition Penalty",
-        )
-    
-    with gr.Row():
-        clear = gr.Button("🗑️ Clear")
-        submit = gr.Button("📤 Send", variant="primary")
-    
-    gr.Examples(
-        examples=examples,
-        inputs=msg,
-        label="Example Questions:",
-    )
-    
-    # Set up event handlers
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-    
-    def bot(history, temp, max_tok, top_p_val, rep_pen):
-        user_message = history[-1][0]
-        bot_response = generate_response(
-            user_message,
-            history[:-1],
-            temperature=temp,
-            max_new_tokens=max_tok,
-            top_p=top_p_val,
-            repetition_penalty=rep_pen,
-        )
-        history[-1][1] = bot_response
-        return history
-    
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, [chatbot, temperature, max_tokens, top_p, rep_penalty], chatbot
-    )
-    submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, [chatbot, temperature, max_tokens, top_p, rep_penalty], chatbot
-    )
-    clear.click(lambda: None, None, chatbot, queue=False)
-    
-    gr.Markdown("""
-    ---
-    ### 📝 Note
-    This model is fine-tuned specifically on Goan Q&A data. Responses are generated based on patterns learned from the training dataset.
-    For best results, ask questions about Goa, its culture, history, cuisine, and related topics.
-    """)
+# Create Gradio interface
+demo = gr.ChatInterface(
+    fn=generate_response,
+    title=TITLE,
+    description=DESCRIPTION,
+    examples=examples,
+    additional_inputs=[
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=1, maximum=512, value=256, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
+        gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty"),
+    ],
+)
 
 if __name__ == "__main__":
     demo.launch()