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import gradio as gr
#from transformers import pipeline
"""
pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
def predict(image):
predictions = pipeline(image)
return {p["label"]: p["score"] for p in predictions}
gr.Interface(
predict,
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()
"""
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)
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