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llama3_qa_bot.py

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+# Import necessary packages
+from ibm_watsonx_ai import Credentials
+from ibm_watsonx_ai import APIClient
+from ibm_watsonx_ai.foundation_models import Model, ModelInference
+from ibm_watsonx_ai.foundation_models.schema import TextChatParameters
+from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
+import gradio as gr
+
+watsonx_API="L0sx3BXcQRWNmz45mbBLxL1UiZGnftHFQTwITAci-523"
+project_id="ed8f7a2c-e597-4a09-a98f-dbdcef57a0d0"
+
+# Set credentials to use the model
+credentials = {
+                   "url" : "https://au-syd.ml.cloud.ibm.com",
+                   "apikey": watsonx_API
+}
+
+# Model and project settings
+model_id = "meta-llama/llama-3-2-11b-vision-instruct"  # Directly specifying the LLAMA3 model
+project_id = project_id # Specifying project_id as provided
+
+params = TextChatParameters(
+    temperature=0.1,
+    max_tokens=512
+)
+
+# Initialize the model
+model = ModelInference(
+    model_id=model_id,
+    credentials=credentials,
+    project_id=project_id,
+    params=params
+)
+
+# Function to generate a response from the model
+def generate_response(prompt_txt):
+    messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": prompt_txt
+            },
+        ]
+    }
+]
+    generated_response = model.chat(messages=messages)
+    generated_text = generated_response['choices'][0]['message']['content']
+
+    return generated_text
+
+# Create Gradio interface
+chat_application = gr.Interface(
+    fn=generate_response,
+    flagging_mode="never",
+    inputs=gr.Textbox(label="Input", lines=2, placeholder="Type your question here..."),
+    outputs=gr.Textbox(label="Output"),
+    title="Watsonx.ai Chatbot",
+    description="Ask any question and the chatbot will try to answer."
+)
+
+# Launch the app
+chat_application.launch(share=True)
+

llama3_qa_bot_requirements.txt

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+pip install gradio ibm_watsonx_ai email-validator
+pip install numpy pandas