add client initialization and dropdown changing model for client

app.py

@@ -11,10 +11,10 @@ from typing import List, Optional, Tuple, Union
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 LEPTON_API_TOKEN = os.environ.get("LEPTON_API_TOKEN", None)
 
-client=openai.OpenAI(
-    base_url="https://yb15a7dy-patronus-lynx-8b-v1-1.tin.lepton.run/api/v1/",
-    api_key=LEPTON_API_TOKEN
-)
+# client=openai.OpenAI(
+#     base_url="https://yb15a7dy-patronus-lynx-8b-v1-1.tin.lepton.run/api/v1/",
+#     api_key=LEPTON_API_TOKEN
+# )
 # client=openai.OpenAI(
 #     base_url="https://yb15a7dy-lynx-70b.tin.lepton.run/api/v1/",
 #     api_key=LEPTON_API_TOKEN
@@ -71,6 +71,18 @@ HEADER = """
 **Getting Started**: Provide a question and document or context given to your model in addition to the answer given by the model and then click submit. The output panel will indicate whether the reponse is a hallucination (Fail) or if it is faithful to the given document or context (Pass) through the score Pass or Fail and provide reasoning behind the score.
 """
 
+def update_client(model_name):
+    if model_name == "Patronus Lynx 8B":
+        return openai.OpenAI(
+            base_url="https://yb15a7dy-patronus-lynx-8b-v1-1.tin.lepton.run/api/v1/",
+            api_key=LEPTON_API_TOKEN
+        )
+    elif model_name == "Patronus Lynx 70B":
+        return openai.OpenAI(
+            base_url="https://yb15a7dy-lynx-70b.tin.lepton.run/api/v1/",
+            api_key=LEPTON_API_TOKEN
+        )
+
 def parse_patronus_lynx_response(
     response: str,
 ) -> Tuple[bool, Union[List[str], None]]:
@@ -97,7 +109,7 @@ def parse_patronus_lynx_response(
 
     return hallucination, reasoning
 
-def model_call(question, document, answer):
+def model_call(question, document, answer, client):
     if question == "" or document == "" or answer == "":
         return "", ""
     NEW_FORMAT = PROMPT.format(question=question, document=document, answer=answer)
@@ -112,17 +124,18 @@ def model_call(question, document, answer):
     combined_reasoning = " ".join(reasoning)[1:-1]
     return combined_reasoning, score
 
-inputs = [
-    gr.Textbox(label="Question"),
-    gr.Textbox(label="Document"),
-    gr.Textbox(label="Answer")
-]
-outputs = [
-    gr.Textbox(label="Reasoning"),
-    gr.Textbox(label="Score")
-]
+# inputs = [
+#     gr.Textbox(label="Question"),
+#     gr.Textbox(label="Document"),
+#     gr.Textbox(label="Answer")
+# ]
+# outputs = [
+#     gr.Textbox(label="Reasoning"),
+#     gr.Textbox(label="Score")
+# ]
 
 with gr.Blocks() as demo:
+    client_state = gr.State(update_client("Patronus Lynx 8B"))
     gr.Markdown(HEADER)
     # gr.Interface(fn=model_call, inputs=inputs, outputs=outputs)
     model_dropdown = gr.Dropdown(choices=["Patronus Lynx 8B", "Patronus Lynx 70B"], value="Patronus Lynx 8B", label="Model", interactive=True)
@@ -138,10 +151,10 @@ with gr.Blocks() as demo:
             reasoning = gr.Textbox(label="Reasoning")
             score = gr.Textbox(label="Score (FAIL if Hallucinated, PASS if not)")
 
-    # model_dropdown.change(fn=update_model, inputs=[model_dropdown, tokenizer_state, model_state], outputs=[tokenizer_state, model_state])
+    model_dropdown.change(fn=update_client, inputs=[model_dropdown], outputs=[client_state])
 
-    submit_button.click(fn=model_call, inputs=[question, document, answer], outputs=[reasoning, score])
-    question.submit(fn=model_call, inputs=[question, document, answer], outputs=[reasoning, score])
-    document.submit(fn=model_call, inputs=[question, document, answer], outputs=[reasoning, score])
-    answer.submit(fn=model_call, inputs=[question, document, answer], outputs=[reasoning, score])
+    submit_button.click(fn=model_call, inputs=[question, document, answer, client_state], outputs=[reasoning, score])
+    question.submit(fn=model_call, inputs=[question, document, answer, client_state], outputs=[reasoning, score])
+    document.submit(fn=model_call, inputs=[question, document, answer, client_state], outputs=[reasoning, score])
+    answer.submit(fn=model_call, inputs=[question, document, answer, client_state], outputs=[reasoning, score])
 demo.launch()