Upload app.py

app.py

@@ -1,63 +1,55 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+# -*- coding: utf-8 -*-
+"""app.ipynb
 
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qIFntwH-_zF7GkQbgjKoXMXnQpZ4HVse
 """
-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("HuggingFaceH4/zephyr-7b-beta")
 
+import gradio as gr
+import streamlit as st
+from transformers import AutoTokenizer,  AutoModelForSequenceClassification
+
+# Load the base model
+base_model_name = "Preetham04/sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+model =  AutoModelForSequenceClassification.from_pretrained(base_model_name)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Load the adapter configuration and model files
+adapter_config_path = "config.json"
+adapter_model_path = "model.safetensors"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# 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)
 
-    messages.append({"role": "user", "content": message})
+# Activate the adapter
+model.set_active_adapters(adapter_name)
 
-    response = ""
+st.title("🤖 Chatbot with Adapter-Enhanced Model")
+st.write("Interact with your custom adapter-enhanced model. Type a message and get responses!")
 
-    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
+# Initialize or retrieve the chat history
+if 'history' not in st.session_state:
+    st.session_state['history'] = []
 
-        response += token
-        yield response
+# Initialize Gradio
+chatbot = Gradio(model=model, tokenizer=tokenizer)
 
-"""
-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)",
-        ),
-    ],
-)
+# 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__":
-    demo.launch()
+    chatbot.run()
+