Upload app.py

app.py

@@ -1,70 +1,35 @@
+import os
+import torch
+import numpy as np
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Change to your Hugging Face model repo ID
+MODEL_ID = "SharvNey/capstone_project"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+def classify_text(text):
+    if not text.strip():
+        return {"🧑 Human-Written": 0.0, "🤖 AI-Generated": 0.0}
+    enc = tokenizer(text, truncation=True, padding=True, max_length=256, return_tensors="pt")
+    enc = {k: v.to(device) for k,v in enc.items()}
+    with torch.no_grad():
+        out = model(**enc)
+        probs = torch.nn.functional.softmax(out.logits, dim=-1).cpu().numpy()[0]
+    return {"🧑 Human-Written": float(probs[0]), "🤖 AI-Generated": float(probs[1])}
+
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=8, placeholder="Paste text here..."),
+    outputs=gr.Label(num_top_classes=2),
+    title="🤖 AI vs Human Text Classifier",
+    description="Fine-tuned RoBERTa model that detects whether text is Human-written 🧑 or AI-generated 🤖"
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
 if __name__ == "__main__":
     demo.launch()