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app.py

@@ -2,10 +2,12 @@ import gradio as gr
 from transformers import pipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Load the model and tokenizer
+# Load the model
 model_name = "AdaptLLM/law-LLM"
 # model_name = "google/gemma-2b"
 # model_name = "mistralai/Mistral-7B-v0.1"
+
+# Tokenizers usage
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
@@ -33,16 +35,46 @@ def chat_interface(input_text):
     response = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
     return response
 
+# Load the Gemma 2B model using the pipeline
+gemma_2b_chatbot = pipeline("text2text-generation", model="google/gemma-2b")
+
+# Load the law-LLM model using the pipeline
+law_llm_chatbot = pipeline("text2text-generation", model="AdaptLLM/law-LLM")
+
+# Define the chat function for Gemma 2B
+def gemma_2b_chat(input_text):
+    response = gemma_2b_chatbot(input_text)[0]["generated_text"]
+    return response
+
+# Define the chat function for law-LLM
+def law_llm_chat(input_text):
+    response = law_llm_chatbot(input_text)[0]["generated_text"]
+    return response
+
+
+# Create the Gradio interface for Gemma 2B
+# gemma_2b_inputs = gr.inputs.Textbox(lines=2, label="User Input")
+# gemma_2b_outputs = gr.outputs.Textbox(label="Chatbot Response")
+# gemma_2b_interface = gr.Interface(fn=gemma_2b_chat, inputs=gemma_2b_inputs, outputs=gemma_2b_outputs)
+
+# Create the Gradio interface for law-LLM
+law_llm_inputs = gr.inputs.Textbox(lines=2, label="User Input")
+law_llm_outputs = gr.outputs.Textbox(label="Chatbot Response")
+law_llm_interface = gr.Interface(fn=law_llm_chat, inputs=law_llm_inputs, outputs=law_llm_outputs)
+
+# Run the Gradio interfaces
+# gemma_2b_interface.launch(share=True)
+law_llm_interface.launch(share=True)
 
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=chat_interface,
-    inputs=gr.inputs.Textbox(lines=2, label="Input Text"),
-    outputs=gr.outputs.Textbox(label="Output Text"),
-    title="Chat Interface",
-    description="Enter text and get a response using the LLM model",
+# Create the Gradio interface with tokenizers
+# iface = gr.Interface(
+#     fn=chat_interface,
+#     inputs=gr.inputs.Textbox(lines=2, label="Input Text"),
+#     outputs=gr.outputs.Textbox(label="Output Text"),
+#     title="Chat Interface",
+#     description="Enter text and get a response using the LLM model",
     # live=True  # Enable live updates
-)
+# )
 
 # Launch the interface using Hugging Face Spaces
-iface.launch(share=True)
+# iface.launch(share=True)