Upload app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import whisper
 from langchain_openai import ChatOpenAI
+from langchain_openai import AzureChatOpenAI
 from utils import RefineDataSummarizer
 from utils import (
     prompt_template,
@@ -47,8 +48,8 @@ def time_stamped_text(transcript_result):
     
 
 def transcript(file_dir, model_type, time_stamp):
-    model_dir = os.path.join('models', model_type)
-    # model_dir = "E:\\Whisper\\" + model_type
+    # model_dir = os.path.join('models', model_type)
+    model_dir = "E:\\Whisper\\" + model_type
     model = whisper.load_model(model_dir)
     result = model.transcribe(file_dir, language='English', task='transcribe')
 
@@ -69,13 +70,25 @@ def upload_file(file_paths):
     return file_paths
 
 
-def summary(text, chunk_num, chunk_overlap, user_api, llm_type, prompt, refine_prompt):
-    if user_api == "Not Provided":
-        api_key = os.getenv("openai_api")
-    else:
-        api_key = user_api
-    api_key = api_key.strip()
-    llm = ChatOpenAI(temperature=1, openai_api_key=api_key, model_name=llm_type)
+def summary(text, chunk_num, chunk_overlap, llm_type, prompt, refine_prompt):
+    #if user_api == "Not Provided":
+        # api_key = os.getenv("openai_api")
+        #deployment_name = llm_type
+    #else:
+    #    api_key = user_api
+    #api_key = api_key.strip()
+    # llm = ChatOpenAI(temperature=1, openai_api_key=api_key, model_name=llm_type)
+
+    os.environ["AZURE_OPENAI_API_KEY"] = os.getenv("azure_api")
+    os.environ["AZURE_OPENAI_ENDPOINT"] =  os.getenv("azure_endpoint")
+    openai_api_version=os.getenv("azure_api_version")
+    deployment_name = llm_type
+
+    llm = AzureChatOpenAI(
+        openai_api_version=openai_api_version,
+        azure_deployment=deployment_name
+    )
+
     rds = RefineDataSummarizer(llm=llm, prompt_template=prompt, refine_template=refine_prompt)
     result = rds.get_summarization(text, chunk_num=chunk_num, chunk_overlap=chunk_overlap)
     text = result["output_text"]
@@ -125,12 +138,20 @@ with gr.Blocks() as demo:
                     )       
 
             with gr.Accordion(open=False, label=["llm settings"]):
-                user_api = gr.Textbox(placeholder="If Empty, Use Default Key", label="Your API Key", value="Not Provided")
+                # user_api = gr.Textbox(placeholder="If Empty, Use Default Key", label="Your API Key", value="Not Provided")
+                # llm_type =  gr.Dropdown(
+                #     [
+                #     "gpt-3.5-turbo", 
+                #     "gpt-3.5-turbo-16k", 
+                #     "gpt-4-1106-preview"
+                #     ], label="LLM Type", value="gpt-4-1106-preview")
                 llm_type =  gr.Dropdown(
                     [
-                    "gpt-3.5-turbo", 
-                    "gpt-3.5-turbo-16k", 
-                    "gpt-4-1106-preview"
+                        "gpt-4-32k",
+                        "gpt-4",
+                        "gpt-4-1106-preview",
+                        "gpt-35-turbo", 
+                        "gpt-35-turbo-16k"
                     ], label="LLM Type", value="gpt-4-1106-preview")
             SunmmaryButton = gr.Button("Summary", variant="primary")
             summary_text = gr.Textbox(placeholder="Summary Result", label="Summary")
@@ -153,7 +174,7 @@ with gr.Blocks() as demo:
             transcript_text,
             chunk_num,
             chunk_overlap,
-            user_api,
+            #user_api,
             llm_type,
             prompt,
             refine_prompt