Create app.py

app.py

@@ -0,0 +1,88 @@
+import time
+import gradio as gr
+from transformers import pipeline
+
+# Load a small, free text generation model
+generator = pipeline("text-generation", model="bigscience/bloomz-560m")
+
+# Load a moderation pipeline
+moderator = pipeline("text-classification", model="unitary/toxic-bert")
+
+# Simple in-memory rate limiter
+request_log = {}
+MAX_REQUESTS = 10       # max requests
+WINDOW_SECONDS = 60    # per 60 seconds
+
+def is_rate_limited(ip):
+    now = time.time()
+    window_start = now - WINDOW_SECONDS
+    history = request_log.get(ip, [])
+    # keep only recent requests
+    history = [t for t in history if t > window_start]
+    if len(history) >= MAX_REQUESTS:
+        return True
+    history.append(now)
+    request_log[ip] = history
+    return False
+
+def contains_profanity(text, threshold=0.5):
+    """
+    Uses a Hugging Face moderation model to detect toxicity.
+    Returns True if the text is likely toxic/profane.
+    """
+    results = moderator(text)
+
+    # results looks like: [{'label': 'toxic', 'score': 0.87}]
+    label = results[0]['label']
+    score = results[0]['score']
+
+    return label.lower() == "toxic" and score >= threshold
+
+# Define your function with some safeguards
+def generate_conclusion(user_input, request: gr.Request):
+    ip = request.client.host if request else "unknown"
+    if is_rate_limited(ip):
+        return "⚠️ Too many requests. Please wait a bit before trying again."
+
+    if not user_input.strip():
+        return "⚠️ Please enter some text."
+
+    if contains_profanity(user_input):
+        return "⚠️ Your input contains inappropriate or toxic language."
+
+    # Limit input length
+    if len(user_input) > 300:
+        return "⚠️ Input too long. Please keep it under 300 characters."
+
+    # Add your instruction prompt
+    prompt = (
+        "Generate only the final conclusion from the following input. "
+        "Do not include elaboration, explanation, or supporting details.\n\n"
+        f"{user_input}"
+    )
+
+    # Generate with limits to prevent abuse
+    try:
+        output = generator(
+            prompt,
+            max_new_tokens=50,   # keep responses short
+            do_sample=True,
+            temperature=0.7,
+            return_full_text=False
+        )
+
+        return output[0]["generated_text"]
+    except Exception as e:
+        return f"⚠️ An error occurred: {str(e)}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=generate_conclusion,
+    inputs=gr.Textbox(lines=4, placeholder="Enter your text here..."),
+    outputs="text",
+    title="Conclusion Generator Demo",
+    description="Generates a short conclusion from your input. Limited to 300 characters."
+)
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=2, max_size=5).launch()