Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import os 
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain_core.prompts import FewShotChatMessagePromptTemplate
+from dotenv import load_dotenv
+
+load_dotenv()
+api_key = os.getenv("GROQ_API_KEY")
+
+example_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("human", "{input}"),
+        ("ai", "{output}"),
+    ]
+)
+
+chat = ChatGroq(model = "mixtral-8x7b-32768", api_key = api_key)
+examples = [
+   {
+            "input": "What does the eligibility verification agent (EVA) do?",
+            "output": "EVA automates the process of verifying a patient’s eligibility and benefits information in real-time, eliminating manual data entry errors and reducing claim rejections."
+        },
+        {
+            "input": "What does the claims processing agent (CAM) do?",
+            "output": "CAM streamlines the submission and management of claims, improving accuracy, reducing manual intervention, and accelerating reimbursements."
+        },
+        {
+            "input": "How does the payment posting agent (PHIL) work?",
+            "output": "PHIL automates the posting of payments to patient accounts, ensuring fast, accurate reconciliation of payments and reducing administrative burden."
+        },
+        {
+            "input": "Tell me about Hub9 AI's Agents.",
+            "output": "Hub9 AI provides a suite of AI-powered automation agents designed to streamline healthcare processes. These include Eligibility Verification (EVA), Claims Processing (CAM), and Payment Posting (PHIL), among others."
+        },
+        {
+            "input": "What are the benefits of using Hub9 AI's agents?",
+            "output": "Using Hub9 AI's Agents can significantly reduce administrative costs, improve operational efficiency, and reduce errors in critical processes like claims management and payment posting."
+        }
+
+]
+
+prompt = FewShotChatMessagePromptTemplate(
+    examples=examples,
+    example_prompt = example_prompt,
+)
+
+final_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You have extensive knowledge of Hub9 AI. DO NOT HALLUCINATE."),
+        prompt,
+        ("human", "{input}"),
+    ]
+)
+
+chain = final_prompt | chat
+
+def response(text):
+    answer = chain.invoke(text)
+    return answer
+
+gr.ChatInterface(
+    fn=response, 
+    type="messages"
+).launch()