Create app.py

app.py

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+import gradio as gr
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain.output_parsers import TextIteratorStreamer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the Hugging Face model and tokenizer
+model_name = "KvrParaskevi/Llama-2-7b-Hotel-Booking-Model"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define the Langchain chatbot function
+def chatbot(message, history):
+    # Create a Langchain prompt template
+    prompt_template = HumanMessagePromptTemplate.from_message(message)
+    # Create a Langchain chat prompt template
+    chat_prompt_template = ChatPromptTemplate.from_messages([prompt_template])
+    # Use the Langchain TextIteratorStreamer to generate responses
+    streamer = TextIteratorStreamer(model, tokenizer, chat_prompt_template)
+    response = streamer.generate()
+    return response
+
+# Create a Gradio chatbot interface
+with gr.Blocks() as demo:
+    chatbot_interface = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    # Define the chatbot function as a Gradio interface
+    demo.chatbot_interface = gr.Interface(
+        fn=chatbot,
+        inputs="text",
+        outputs="text",
+        title="Langchain Chatbot",
+        description="A simple chatbot using Langchain and Hugging Face"
+    )
+
+    # Launch the Gradio app
+    demo.launch()