update

app.py

@@ -1,68 +1,12 @@
-import os
 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",token=os.getenv('HF_API_TOKEN'))
+client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
 
-# ## 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()
-
-
-####19
+## None type 
 def respond(
     message: str,
     history: list[tuple[str, str]],  # This will not be used
@@ -71,72 +15,39 @@ def respond(
     temperature: float,
     top_p: float,
 ):
-    # Combine the system message and user input into a single prompt
-    prompt = f"{system_message}\n{message}"
+    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 using text generation method
-        for message in client.text_generation(
-            prompt=prompt,
-            max_new_tokens=max_tokens,
+        # 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.token is not None:
-                response += message.token
+            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}"
 
-# Define the Gradio interface
-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)",
-        ),
+"""
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
     ],
 )
 
-if __name__ == "__main__":
-    demo.launch()
 
-# import os
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# # Initialize the Hugging Face Inference Client
-# client = InferenceClient(
-#     "meta-llama/Meta-Llama-3.1-8B-Instruct",
-#     token=  os.getenv("HF_API_TOKEN"),# Replace with your actual token
-# )
-
-# # Define a function to handle the chat input and get a response from the model
-# def chat_with_model(user_input):
-#     # Call the client to get the model's response
-#     response = ""
-#     for message in client.chat_completion(
-#         messages=[{"role": "user", "content": user_input}],
-#         max_tokens=500,
-#         stream=True,
-#     ):
-#         response += message.choices[0].delta.content
-#     return response
-
-# # Create a Gradio interface with a chat component
-# with gr.Blocks() as demo:
-#     chatbot = gr.Chatbot()
-#     with gr.Row():
-#         txt = gr.Textbox(show_label=False, placeholder="Type your message here...")
-#         txt.submit(chat_with_model, inputs=txt, outputs=chatbot)
-
-# demo.launch()
+if __name__ == "__main__":
+    demo.launch()