Hotdog + Chatbot

app.py

@@ -1,18 +1,33 @@
 import gradio as gr
 from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+pipeline_hotdog = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+chatbot_pipeline = pipeline(task="text-generation", model="microsoft/DialoGPT-medium")
 
+def predict(input_img,chat_input):
+    hotdog_predictions = pipeline_hotdog(input_img)
+        # Generate chatbot response
+    chatbot_response = chatbot_pipeline(chat_input, max_length=50)[0]['generated_text']
+
+    return input_img, {
+        "Hotdog Classification": {p["label"]: p["score"] for p in hotdog_predictions},
+        "Chatbot Response": chatbot_response
+    }
+    
 gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Label(label="Result", num_top_classes=1)],
-    title="Hot Dog? Or Not?",
+    fn=predict,
+    inputs=[
+        gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+        gr.Textbox(label="Chatbot Input", placeholder="Type something to chat with the bot")
+    ],
+    outputs=[
+        gr.Image(label="Processed Image"),
+        gr.Label(label="Hotdog Classification", num_top_classes=2),
+        gr.Textbox(label="Chatbot Response", output_transform=lambda x: x["Chatbot Response"], type="readonly")
+    ],
+    title="Hot Dog? Or Not? with Chatbot",
 )
 
-if __name__ == "__main__":
+if _name_ == "_main_":
     gradio_app.launch()