Update app.py

app.py

@@ -1,64 +1,49 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("gemma-3-4b-it")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = 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)",
-        ),
-    ],
-)
-
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer from Hugging Face
+model_name = "google/gemma-3-1b-it"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def respond(user_input, chat_history):
+    """
+    Generates the chatbot's response.
+    
+    It builds a conversation string from the chat history,
+    appends the latest user input, and generates text using the model.
+    """
+    conversation = ""
+    # Build a conversational prompt from past messages
+    for user_msg, bot_msg in chat_history:
+        conversation += f"User: {user_msg}\nBot: {bot_msg}\n"
+    conversation += f"User: {user_input}\nBot: "
+
+    # Tokenize the conversation prompt and generate a response
+    inputs = tokenizer.encode(conversation, return_tensors="pt")
+    outputs = model.generate(
+        inputs,
+        max_length=inputs.shape[1] + 100,  # adjust max_length as needed
+        do_sample=True,
+        temperature=0.7,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # The generated text includes the prompt text, so extract only the new reply.
+    bot_reply = generated_text[len(conversation):].split("User:", 1)[0].strip()
+    chat_history.append((user_input, bot_reply))
+    return "", chat_history  # Clear input and return updated chat history
+
+# Build the Gradio interface using Blocks for a conversational layout.
+with gr.Blocks() as demo:
+    gr.Markdown("# Chatbot powered by google/gemma-3-1b-it")
+    chatbot = gr.Chatbot()
+    state = gr.State([])  # To keep track of the conversation history.
+    txt = gr.Textbox(show_label=True, placeholder="Type a message here...", label="Your Message")
+    
+    # When the user submits a message, call the respond() function.
+    txt.submit(respond, inputs=[txt, state], outputs=[txt, chatbot])
 
 if __name__ == "__main__":
     demo.launch()