Update Gradio interface for warning lamp detector with image upload functionality

app.py

@@ -1,64 +1,82 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
 
 """
-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
+Warning Lamp Detector using Hugging Face Inference API
+This application allows users to upload images of warning lamps and get classification results.
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Initialize the client with your model
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def detect_warning_lamp(image, history: list[tuple[str, str]], system_message):
+    """
+    Process the uploaded image and return detection results
+    """
+    # TODO: Replace with actual model inference
+    # This is a placeholder response - you'll need to integrate your actual model
     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]})
-
-    messages.append({"role": "user", "content": message})
+    
+    # Add the image analysis request
+    messages.append({
+        "role": "user", 
+        "content": f"Please analyze this warning lamp image and provide a detailed classification."
+    })
 
     response = ""
-
     for message in client.chat_completion(
         messages,
-        max_tokens=max_tokens,
+        max_tokens=512,
         stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        temperature=0.7,
+        top_p=0.95,
     ):
         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)",
-        ),
-    ],
-)
-
+# Create a custom interface with image upload
+with gr.Blocks(title="Warning Lamp Detector", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🚨 Warning Lamp Detector
+    Upload an image of a warning lamp to get its classification.
+    
+    ### Instructions:
+    1. Upload a clear image of the warning lamp
+    2. Wait for the analysis
+    3. View the detailed classification results
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                label="Upload Warning Lamp Image",
+                type="pil",
+                tool="select"
+            )
+            system_message = gr.Textbox(
+                value="You are an expert in warning lamp classification. Analyze the image and provide detailed information about the type, color, and status of the warning lamp.",
+                label="System Message",
+                lines=3
+            )
+        
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(
+                [],
+                elem_id="chatbot",
+                bubble_full_width=False,
+                avatar_images=(None, "🚨"),
+                height=400
+            )
+    
+    # Add a submit button
+    submit_btn = gr.Button("Analyze Warning Lamp", variant="primary")
+    submit_btn.click(
+        detect_warning_lamp,
+        inputs=[image_input, chatbot, system_message],
+        outputs=chatbot
+    )
 
 if __name__ == "__main__":
     demo.launch()