Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+
+# Load the tokenizer and model for t5-base
+tokenizer = T5Tokenizer.from_pretrained("t5-base")
+model = T5ForConditionalGeneration.from_pretrained("t5-base")
+
+# Set up conversational memory using LangChain's ConversationBufferMemory
+memory = ConversationBufferMemory()
+
+# Define the chatbot function with memory
+def chat_with_t5(input_text):
+    # Retrieve conversation history and append the current user input
+    conversation_history = memory.load_memory_variables({})['history']
+    
+    # Combine the history with the current user input
+    # For regular T5, we need to prompt the model differently since it's not instruction-tuned like FLAN-T5
+    # Using a simple summarization prompt format as an example, you can modify as needed
+    full_input = f"User: {input_text}\nAssistant:"
+    
+    if conversation_history:
+        full_input = f"Previous conversation: {conversation_history}\n{full_input}"
+    
+    # Tokenize the input for the model
+    input_ids = tokenizer.encode(full_input, return_tensors="pt")
+    
+    # Generate the response from the model
+    outputs = model.generate(input_ids, max_length=200, num_return_sequences=1)
+    
+    # Decode the model output
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Update the memory with the user input and model response
+    memory.save_context({"input": input_text}, {"output": response})
+    
+    return response
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=chat_with_t5,
+    inputs=gr.Textbox(label="Chat with T5-Base"),
+    outputs=gr.Textbox(label="T5-Base's Response"),
+    title="T5-Base Chatbot with Memory",
+    description="This is a simple chatbot powered by the T5-base model with conversational memory, using LangChain.",
+)
+
+# Launch the Gradio app
+interface.launch()