Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
-from transformers import pipeline
+#from transformers import pipeline
 
+"""
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
 def predict(image):
@@ -12,4 +13,33 @@ gr.Interface(
     inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
     outputs=gr.outputs.Label(num_top_classes=2),
     title="Hot Dog? Or Not?",
-).launch()
+).launch()
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def chatbot_response(user_message):
+    # Load the pre-trained model and tokenizer
+    model_name = "your_pretrained_model_name"  # Replace with the name of the pre-trained model you want to use
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+
+    # Tokenize the user's message and generate the response
+    inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return response
+
+if __name__ == '__main__':
+    print("Chatbot: Hello! I'm your chatbot. Type 'exit' to end the conversation.")
+    
+    while True:
+        user_input = input("You: ")
+        
+        if user_input.lower() == 'exit':
+            break
+
+        response = chatbot_response(user_input)
+        print("Chatbot:", response)
+