Create app.py

app.py

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+"""
+author: Elena Lowery
+
+This code sample shows how to invoke Large Language Models (LLMs) deployed in watsonx.ai.
+Documentation: https://ibm.github.io/watson-machine-learning-sdk/foundation_models.html
+You will need to provide your IBM Cloud API key and a watonx.ai project id  (any project)
+for accessing watsonx.ai in a .env file
+This example shows simple use cases without comprehensive prompt tuning
+"""
+
+# Install the wml api in your Python environment prior to running this example:
+# pip install ibm-watson-machine-learning
+# pip install ibm-cloud-sdk-core
+# pip install python-dotenv
+# pip install gradio
+
+# For reading credentials from the .env file
+import os
+from dotenv import load_dotenv
+
+# WML python SDK
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes, DecodingMethods
+
+# For invocation of LLM with REST API
+import requests, json
+from ibm_cloud_sdk_core import IAMTokenManager
+
+# For creating Gradio interface
+import gradio as gr
+
+# URL of the hosted LLMs is hardcoded because at this time all LLMs share the same endpoint
+url = "https://us-south.ml.cloud.ibm.com"
+
+# These global variables will be updated in get_credentials() functions
+watsonx_project_id = ""
+# Replace with your IBM Cloud key
+api_key = ""
+
+def get_credentials():
+    load_dotenv()
+    # Update the global variables that will be used for authentication in another function
+    globals()["api_key"] = os.getenv("api_key", None)
+    globals()["watsonx_project_id"] = os.getenv("project_id", None)
+
+# The get_model function creates an LLM model object with the specified parameters
+def get_model(model_type, max_tokens, min_tokens, decoding, temperature):
+    generate_params = {
+        GenParams.MAX_NEW_TOKENS: max_tokens,
+        GenParams.MIN_NEW_TOKENS: min_tokens,
+        GenParams.DECODING_METHOD: decoding,
+        GenParams.TEMPERATURE: temperature
+    }
+
+    model = Model(
+        model_id=model_type,
+        params=generate_params,
+        credentials={
+            "apikey": api_key,
+            "url": url
+        },
+        project_id=watsonx_project_id
+    )
+
+    return model
+
+def generate_response(model_type, prompt, max_tokens, min_tokens, decoding, temperature):
+    model = get_model(model_type, max_tokens, min_tokens, decoding, temperature)
+    generated_response = model.generate(prompt=prompt)
+    return generated_response['results'][0]['generated_text']
+
+def demo_LLM_invocation(prompt, model_type="google/flan-ul2", max_tokens=300, min_tokens=50, decoding="sample", temperature=0.7):
+    get_credentials()
+    response = generate_response(model_type, prompt, max_tokens, min_tokens, decoding, temperature)
+    return response
+
+# Gradio interface
+def gradio_interface(prompt):
+    response = demo_LLM_invocation(prompt)
+    return response
+
+# Create a Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs="text",
+    outputs="text",
+    title="🌠 Test watsonx.ai LLM",
+    description="Ask a question and get a response from the IBM Watson LLM. For example: 'What is IBM?'"
+)
+
+if __name__ == "__main__":
+    iface.launch()