Update app.py

app.py

@@ -1,61 +1,76 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("meta-llama/Meta-Llama-3-8B")
-
-## None type 
-def respond(
-    message: str,
-    history: list[tuple[str, str]],  # This will not be used
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-):
-    messages = [{"role": "system", "content": system_message}]
+# import gradio as gr
+# from huggingface_hub import InferenceClient
+
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
+# client = InferenceClient("meta-llama/Meta-Llama-3-8B")
+
+# ## None type 
+# def respond(
+#     message: str,
+#     history: list[tuple[str, str]],  # This will not be used
+#     system_message: str,
+#     max_tokens: int,
+#     temperature: float,
+#     top_p: float,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
     
-    # Append only the latest user message
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    try:
-        # Generate response from the model
-        for message in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            if message.choices[0].delta.content is not None:
-                token = message.choices[0].delta.content
-                response += token
-            yield response
-    except Exception as e:
-        yield f"An error occurred: {e}"
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch()
+#     # Append only the latest user message
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     try:
+#         # Generate response from the model
+#         for message in client.chat_completion(
+#             messages,
+#             max_tokens=max_tokens,
+#             stream=True,
+#             temperature=temperature,
+#             top_p=top_p,
+#         ):
+#             if message.choices[0].delta.content is not None:
+#                 token = message.choices[0].delta.content
+#                 response += token
+#             yield response
+#     except Exception as e:
+#         yield f"An error occurred: {e}"
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-405B"
+headers = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+	
+output = query({
+	"inputs": "Can you please let us know more details about your ",
+})