Runtime capacity error Bug fixed by Amit Kamal

app.py

@@ -1,12 +1,14 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+# 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-Instruct")
+# """
+# 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-Instruct")
 
 
+
+# ##None type 
 # def respond(
 #     message,
 #     history: list[tuple[str, str]],
@@ -34,44 +36,76 @@ client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
 #         temperature=temperature,
 #         top_p=top_p,
 #     ):
-#         token = message.choices[0].delta.content
+#         if message.choices[0].delta.content is not None:
+#             token = message.choices[0].delta.content
+#             response += token
+
+#     yield response
+
+# """
+# 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()
+
 
-#         response += token
-#         yield response
 
-##None type 
+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-Instruct")
+
+## None type 
 def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    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}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
+    
+    # Append only the latest user message
     messages.append({"role": "user", "content": message})
 
     response = ""
 
-    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
+    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
@@ -92,6 +126,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()