Create app.py

app.py

@@ -0,0 +1,47 @@
+import os
+from dotenv import load_dotenv
+import gradio as gr
+from langchain_huggingface import HuggingFaceEndpoint
+
+# Load environment variables
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Initialize the Hugging Face endpoint for inference
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Meta-Llama-3-8B",  # Replace with your model repo
+    huggingfacehub_api_token=HF_TOKEN.strip(),
+    temperature=0.7,
+    max_new_tokens=200
+)
+
+# Function to handle chatbot response
+def chatbot_response(message):
+    try:
+        response = llm(message)
+        return response
+    except Exception as e:
+        return f"Error: {e}"
+
+
+# Gradio Interface for Chatbot without Guardrails
+with gr.Blocks() as app_without_guardrails:
+    gr.Markdown("## Chatbot Without Guardrails")
+    gr.Markdown("This chatbot uses the model directly without applying any content filtering.")
+
+    # Input and output
+    with gr.Row():
+        user_input = gr.Textbox(label="Your Message", placeholder="Type here...")
+    response_output = gr.Textbox(label="Response", placeholder="Bot will respond here...")
+    submit_button = gr.Button("Send")
+
+    # Button click event
+    submit_button.click(
+        chatbot_response,
+        inputs=[user_input],
+        outputs=[response_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app_without_guardrails.launch()