Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qIFntwH-_zF7GkQbgjKoXMXnQpZ4HVse
+"""
+
+import gradio as gr
+import streamlit as st
+from transformers import AutoTokenizer,  AutoModelForSequenceClassification
+
+# Load the base model
+base_model_name = "Preetham04/Preetham04-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+model =  AutoModelForSequenceClassification.from_pretrained(base_model_name)
+
+# Load the adapter configuration and model files
+adapter_config_path = "config.json"
+adapter_model_path = "model.safetensors"
+
+# Load the adapter into the model
+adapter_name = "custom_adapter"  # Define your adapter name
+model.load_adapter(adapter_config_path, model_file=adapter_model_path, load_as=adapter_name)
+
+# Activate the adapter
+model.set_active_adapters(adapter_name)
+
+st.title("🤖 Chatbot with Adapter-Enhanced Model")
+st.write("Interact with your custom adapter-enhanced model. Type a message and get responses!")
+
+# Initialize or retrieve the chat history
+if 'history' not in st.session_state:
+    st.session_state['history'] = []
+
+# Initialize Gradio
+chatbot = Gradio(model=model, tokenizer=tokenizer)
+
+# Define responses for greetings
+@chatbot.on_event("welcome")
+def welcome_handler(payload):
+    return "Welcome! Type a message and get responses from the chatbot."
+
+# Define responses for user messages
+@chatbot.on_message
+def message_handler(payload):
+    user_input = payload["message"]
+    response = chatbot.generate_response(user_input)
+    return response
+
+# Run Gradio
+if __name__ == "__main__":
+    chatbot.run()
+