Update app.py

app.py

@@ -4,26 +4,40 @@ from huggingface_hub import InferenceClient
 # Initialize the inference client with the model
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
+def classify_text(
+    text,
     max_tokens,
     temperature,
-    top_p,
+    top_p
 ):
+    # Define system message with instructions for the model
+    system_message = (
+        "You are tasked with labeling text data based on both emotion temperature and text type categories. "
+        "The final output must be a 13-character code that consists of the following structure:\n\n"
+        "   0 index : Emotion Temperature code\n"
+        "   1 index : Informative\n"
+        "   2 index : Hate\n"
+        "   3 index : Toxic\n"
+        "   4 index : Appreciation\n"
+        "   5 index : Constructive Criticism\n"
+        "   6 index : Genuine Questions\n"
+        "   7 index : Advice/Suggestions\n"
+        "   8 index : Requests\n"
+        "   9 index : Spam\n"
+        "   10 index : Off-Topic\n"
+        "   11 index : Engagement Boosters\n"
+        "   12 index : Neutral/General\n\n"
+        "Every index should have a number between 0-9. 0 means not applicable, 4 means normal, 9 means high. "
+        "Choose appropriate numbers to showcase how much each category is related to the text input.\n\n"
+        "Output Format:\n"
+        "Always return a 13-character code following this structure."
+    )
+
     # Prepare the messages, starting with the system message
     messages = [{"role": "system", "content": system_message}]
 
-    # Add the conversation history to the messages
-    for user_message, assistant_response in history:
-        if user_message:
-            messages.append({"role": "user", "content": user_message})
-        if assistant_response:
-            messages.append({"role": "assistant", "content": assistant_response})
-
-    # Add the current user message
-    messages.append({"role": "user", "content": message})
+    # Add the user message
+    messages.append({"role": "user", "content": text})
 
     response = ""
 
@@ -44,43 +58,14 @@ def respond(
     else:
         return "Invalid response format"
 
-# Instructions for the model
-instructions = (
-    "You are tasked with labeling text data based on both emotion temperature and text type categories. "
-    "The final output must be a 13-character code that consists of the following structure:\n\n"
-    "   0 index : Emotion Temperature code\n"
-    "   1 index : Informative\n"
-    "   2 index : Hate\n"
-    "   3 index : Toxic\n"
-    "   4 index : Appreciation\n"
-    "   5 index : Constructive Criticism\n"
-    "   6 index : Genuine Questions\n"
-    "   7 index : Advice/Suggestions\n"
-    "   8 index : Requests\n"
-    "   9 index : Spam\n"
-    "   10 index : Off-Topic\n"
-    "   11 index : Engagement Boosters\n"
-    "   12 index : Neutral/General\n\n"
-    "Every index should have a number between 0-9. 0 means not applicable, 4 means normal, 9 means high. "
-    "Choose appropriate numbers to showcase how much each category is related to the text input.\n\n"
-    "Example:\n"
-    "For the text 'I love your videos but still something is missing':\n"
-    "   - Emotion: Cold and Warm (CW)\n"
-    "   - Types Detected: 2 (Appreciation), 3 (Constructive Criticism), 5 (Advice/Suggestions)\n"
-    "   - Special Categories: Neutral/General (set the 10th digit to 1), no Hate\n\n"
-    "The output would be: CW02305000010\n\n"
-    "Output Format:\n"
-    "Always return a 13-character code following this structure."
-)
-
 # Create the Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=[
         gr.Textbox(
-            value=instructions,
-            label="Instructions",
-            lines=15,
+            lines=5,
+            label="Input Comment",
+            placeholder="Enter a comment to classify",
         ),
         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"),
@@ -92,6 +77,10 @@ demo = gr.ChatInterface(
             label="Top-p (nucleus sampling)",
         ),
     ],
+    outputs=gr.Textbox(
+        label="13-Character Code",
+        placeholder="The generated 13-character code will appear here",
+    ),
 )
 
 if __name__ == "__main__":