Update app.py

app.py

@@ -1,70 +1,79 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
+# Load Anki-2.5 model
+model_name = "anktechsol/anki-2.5"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float32,
+    device_map="auto"
+)
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+def respond(message, history, system_message, max_tokens, temperature, top_p):
     """
-    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
+    Educational Tutor for Indian Students powered by Anki-2.5
+    Helps with homework, exam prep, and learning in Indian languages
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
+    # Construct conversation with system message
+    conversation = [{"role": "system", "content": system_message}]
+    
+    # Add history
+    for msg in history:
+        if isinstance(msg, dict):
+            conversation.append(msg)
+        elif len(msg) == 2:
+            conversation.append({"role": "user", "content": msg[0]})
+            conversation.append({"role": "assistant", "content": msg[1]})
+    
+    # Add current message
+    conversation.append({"role": "user", "content": message})
+    
+    # Generate response
+    inputs = tokenizer.apply_chat_template(
+        conversation,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+    return response
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create ChatInterface focused on education
 chatbot = gr.ChatInterface(
     respond,
-    type="messages",
+    title="🎓 Indian Education Tutor - Powered by Anki-2.5",
+    description="Get help with homework, exam preparation, and learning - in Hindi, English, and other Indian languages. Ask questions about Math, Science, History, and more!",
+    examples=[
+        ["मुझे पाइथागोरस प्रमेय समझाओ"],
+        ["Explain photosynthesis in simple terms"],
+        ["ভারতের স্বাধীনতা আন্দোলন সম্পর্ভে বলুন"],
+        ["Help me solve this quadratic equation: x^2 + 5x + 6 = 0"],
+        ["நான் கட்டுரை எழுத எப்படி வேணும்?"],
+    ],
     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)",
+        gr.Textbox(
+            value="You are a patient and knowledgeable educational tutor for Indian students. You can explain concepts in Hindi, English, and other Indian languages. You help with homework, exam preparation, and make learning engaging. Always provide clear, step-by-step explanations.",
+            label="System Message"
         ),
+        gr.Slider(minimum=50, maximum=1024, value=512, step=1, label="Max Tokens"),
+        gr.Slider(minimum=0.1, maximum=1.5, 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"),
     ],
+    theme=gr.themes.Soft(),
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
 if __name__ == "__main__":
-    demo.launch()
+    chatbot.launch()