f

app.py

@@ -1,37 +1,27 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Specify the model paths
-base_model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
-fine_tuned_model_name = "cheberle/autotrain-35swc-b4r9z"
-
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
-
-# Load the base model with fine-tuned weights
-model = AutoModelForCausalLM.from_pretrained(
-    fine_tuned_model_name,
-    device_map="auto",
-    torch_dtype="auto",
-    trust_remote_code=True
-)
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Define a simple function for chat
-def chat(input_text):
-    inputs = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
-    outputs = model.generate(inputs, max_length=100, temperature=0.7)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+# Load the model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# Gradio UI
-import gradio as gr
+def classify_text(input_text):
+    # Tokenize the input
+    inputs = tokenizer(input_text, return_tensors="pt")
+    # Get predictions
+    outputs = model(**inputs)
+    probabilities = outputs.logits.softmax(dim=-1).detach().numpy()
+    return {f"Class {i}": prob for i, prob in enumerate(probabilities[0])}
 
+# Create the Gradio interface
 interface = gr.Interface(
-    fn=chat,
-    inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
-    outputs="text",
-    title="Chat with DeepSeek Fine-tuned Model",
-    description="This is a fine-tuned version of the DeepSeek R1 Distill Qwen-7B model. Ask me anything!"
+    fn=classify_text,
+    inputs=gr.Textbox(label="Enter Text"),
+    outputs=gr.Label(label="Class Probabilities"),
+    title="DeepSeek-R1 Text Classification",
+    description="A text classification app powered by DeepSeek-R1."
 )
 
-if __name__ == "__main__":
-    interface.launch()
+# Launch the app
+interface.launch()

requirements.txt

@@ -1,5 +1,5 @@
 huggingface_hub==0.25.2
 transformers
-accelerate
-gradio
-torch
+torch
+datasets
+scipy