again new update

app.py

@@ -1,25 +1,27 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from llama_cpp import Llama
 
-# Load your fine-tuned model
-model_name = "SupermanRX/moderateTherapistModel"  # Replace with your Hugging Face model path
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Load the GGUF model
+model_path = "model.gguf"  # Ensure this matches your uploaded file name
+llm = Llama(model_path=model_path)
 
+# Define the chatbot function
 def chatbot(input_text):
-    # Process user input and generate response
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=200, pad_token_id=tokenizer.eos_token_id)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+    output = llm(prompt=input_text, max_tokens=200)
+    return output['choices'][0]['text']
 
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=chatbot,
-    inputs="text",
-    outputs="text",
-    title="Chat with Your Model"
-)
+# Create Gradio interface
+with gr.Blocks() as demo:
+    chatbot_ui = gr.Chatbot()
+    textbox = gr.Textbox(label="Type your message here:")
+    submit = gr.Button("Send")
 
-# Launch the Gradio app
-interface.launch()
+    # Handle user interaction
+    def user_interaction(input_text, chat_history):
+        response = chatbot(input_text)
+        chat_history.append((input_text, response))
+        return chat_history, ""
+
+    submit.click(user_interaction, [textbox, chatbot_ui], [chatbot_ui, textbox])
+
+demo.launch()