upload all related file to the app

app.py

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+import gradio as gr
+
+from utils import format_as_chat
+from call_api import generate_output
+
+
+def translate(sentence,history,target_language):
+    prompt = f" Translate this sentence into {target_language}: '{sentence}. Please output only the translated sentence in {target_language}!"
+    chat_format = format_as_chat(prompt, history)
+    # print(chat_format)
+    payload = {
+        "inputs": chat_format,
+        "parameters": {
+            "do_sample": False,
+            "max_new_tokens": 400
+        }
+    }
+    # print(payload)
+    response = generate_output(payload)
+    output = response['generated_text']
+    # print(response)
+    parts = output.split('assistant\n\n')
+    return parts[-1].strip()
+
+
+# res = translate("Awesome, Now I can focus on my career without repetition.",'Chinese',[])
+# print(f"Translated result: {res}")
+
+with gr.Blocks() as demo:
+    system_prompt = gr.Textbox(value="German", label = "Target Language")
+
+    gr.ChatInterface(
+        translate,
+        additional_inputs=[system_prompt],
+        examples=[
+        ["Today is Friday!", "German"], ["Let's have fun.","Chinese"], ["See you tomorrow.","Arabic"]],
+        description="Enter an English sentence, choose a target language, I will translate it into the target language for you.",
+        title="Llama 3 8B Instruct, Machine Translation from English into any other language."
+    )
+
+demo.launch(share=True)

call_api.py

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+import requests
+
+
+## Call the API using Python
+def generate_output(payload):
+
+    # Sending the request
+    response = requests.post('https://uf9t072wj5ki2ho4.eu-west-1.aws.endpoints.huggingface.cloud/generate', json=payload)
+
+    # Handling the response
+    data = response.json()
+
+    return data
+
+
+
+
+
+# Payload for the request
+#
+# payload1 = {
+#     "inputs": "Howdy!",
+#     "parameters": {
+#         "do_sample": False,
+#         "max_new_tokens": 40
+#     }
+# }
+#
+# output1 = generate_output(payload1)
+# print(f"output1: {output1}")
+#
+# formatted_input = (
+#     "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHowdy!<|eot_id|>"
+# )
+# payload2 = {
+#     "inputs": formatted_input,
+#     "parameters": {
+#         "do_sample": False,
+#         "max_new_tokens": 40
+#     }
+# }
+#
+#
+# output2 = generate_output(payload2)
+# print(f"output2: {output2}")
+#
+# multi_turn_input = "<|begin_of_text|>" \
+#                    "<|start_header_id|>user<|end_header_id|>\n\nHowdy!<|eot_id|>" \
+#                    "<|start_header_id|>assistant<|end_header_id|>\n\nHowdy back atcha! What brings you to these here parts?<|eot_id|>" \
+#                    "<|start_header_id|>user<|end_header_id|>\n\nMy assignments!<|eot_id|>"
+#
+# payload3 = {
+#     "inputs": multi_turn_input,
+#     "parameters": {
+#         "do_sample": False,
+#         "max_new_tokens": 40
+#     }
+# }
+#
+#
+# output3 = generate_output(payload3)
+# print(f"output3: {output3}")
+#

utils.py

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+from typing import List
+
+
+def format_as_chat(message: str, history: List[List[str]]) -> str:
+    """
+    Given a message and a history of previous messages, returns a string that formats the conversation as a chat.
+    Uses the format expected by Meta Llama 3 Instruct.
+
+    :param message: A string containing the user's most recent message
+    :param history: A list of lists of previous messages, where each sublist is a conversation turn:
+        [[user_message1, assistant_reply1], [user_message2, assistant_reply2], ...]
+    """
+    chat_format = "<|begin_of_text|>"
+    if len(history) > 0:
+        for turn in history:
+            user_message, assistant_message = turn
+            chat_format += f"<|start_header_id|>user<|end_header_id|>\n\n{user_message}<|eot_id|>"
+            chat_format += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_message}<|eot_id|>"
+
+    # Append the most recent user message
+    chat_format += f"<|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|>"
+    return chat_format