Upload app (14).py

app (14).py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)  # Use AutoTokenizer to automatically detect the correct tokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name)  # Use AutoModelForCausalLM for causal language models
+
+def generate_response(message, history):
+    # Combine the conversation history with the new message
+    input_text = f"{message}"
+    
+    # Tokenize input text
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    
+    # Generate response using the model
+    outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
+    
+    # Decode generated text
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
+
+# Create ChatInterface
+demo = gr.ChatInterface(
+    fn=generate_response,
+    title="Chat with DeepSeek",
+    description="A simple chatbot powered by DeepSeek."
+)
+
+# Launch the app
+demo.launch()