Upload app.py

app.py

@@ -0,0 +1,34 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+import gradio as gr
+
+# Step 1: Define the model name from Hugging Face Hub
+model_name = "deepseek-ai/deepseek-7b-instruct"
+
+# Step 2: Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,  # Use float16 for efficiency
+    device_map="auto"           # Automatically assigns GPU or CPU
+)
+
+# Step 3: Define a simple function to generate model responses
+def chat_function(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_length=300, do_sample=True, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Step 4: Create the Gradio interface
+iface = gr.Interface(
+    fn=chat_function,
+    inputs=gr.Textbox(lines=5, placeholder="Type your question here..."),
+    outputs="text",
+    title="🦾 DeepSeek LLM Assistant",
+    description="Ask me anything! Powered by DeepSeek-7B-Instruct 🪐"
+)
+
+# Step 5: Launch the app
+if __name__ == "__main__":
+    iface.launch()