Update app.py

app.py

@@ -48,36 +48,36 @@ with demo:
                         interactive=True,
                     )
         
-        #chat_model_selection = chat_model_dropdown.value
-        chat_model_selection = 'Intel/neural-chat-7b-v1-1'
-        
-        def call_api_and_stream_response(query, chat_model):
-            """
-            Call the API endpoint and yield characters as they are received.
-            This function simulates streaming by yielding characters one by one.
-            """
-            url = inference_endpoint_url
-            params = {"query": query,"selected_model":chat_model}
-            with requests.get(url, json=params, stream=True) as r:
-                for chunk in r.iter_content(chunk_size=1):
-                    if chunk:
-                        yield chunk.decode()
-
-        def get_response(query, history):
-            """
-            Wrapper function to call the streaming API and compile the response.
-            """
-            response = ''
-            
-            global chat_model_selection
-            
-            for char in call_api_and_stream_response(query, chat_model=chat_model_selection):
-                if char == '<':
-                    break
-                response += char
-                yield response
-        
-        gr.ChatInterface(get_response, retry_btn = None, undo_btn=None, concurrency_limit=inference_concurrency_limit).launch()   
+   #    #chat_model_selection = chat_model_dropdown.value
+   #    chat_model_selection = 'Intel/neural-chat-7b-v1-1'
+   #    
+   #    def call_api_and_stream_response(query, chat_model):
+   #        """
+   #        Call the API endpoint and yield characters as they are received.
+   #        This function simulates streaming by yielding characters one by one.
+   #        """
+   #        url = inference_endpoint_url
+   #        params = {"query": query,"selected_model":chat_model}
+   #        with requests.get(url, json=params, stream=True) as r:
+   #            for chunk in r.iter_content(chunk_size=1):
+   #                if chunk:
+   #                    yield chunk.decode()
+#
+   #    def get_response(query, history):
+   #        """
+   #        Wrapper function to call the streaming API and compile the response.
+   #        """
+   #        response = ''
+   #        
+   #        global chat_model_selection
+   #        
+   #        for char in call_api_and_stream_response(query, chat_model=chat_model_selection):
+   #            if char == '<':
+   #                break
+   #            response += char
+   #            yield response
+   #    
+   #    gr.ChatInterface(get_response, retry_btn = None, undo_btn=None, concurrency_limit=inference_concurrency_limit).launch()   
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏆 LLM Leadeboard", elem_id="llm-benchmark-table", id=0):