Update app.py

app.py

@@ -1,64 +1,31 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        response += token
-        yield response
-
-
-"""
-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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import streamlit as st
+from transformers import pipeline
+from datasets import load_dataset
+
+# Initialize text-generation pipeline with the model
+model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
+pipe = pipeline("text-generation", model=model_name)
+
+# Load the dataset
+ds = load_dataset("refugee-law-lab/canadian-legal-data", "default", split="train")
+
+# Streamlit interface
+st.title("Canadian Legal Text Generator")
+st.write("Enter a prompt related to Canadian legal data and generate text using Llama-3.1.")
+
+# Show dataset sample
+st.subheader("Sample Data from Canadian Legal Dataset:")
+st.write(ds[:5])  # Displaying the first 5 rows of the dataset
+
+# Prompt input
+prompt = st.text_area("Enter your prompt:", placeholder="Type something...")
+
+if st.button("Generate Response"):
+    if prompt:
+        # Generate text based on the prompt
+        with st.spinner("Generating response..."):
+            generated_text = pipe(prompt, max_length=100, do_sample=True, temperature=0.7)[0]["generated_text"]
+            st.write("**Generated Text:**")
+            st.write(generated_text)
+    else:
+        st.write("Please enter a prompt to generate a response.")