Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from transformers import pipeline
+
+# Initialize Hugging Face pipelines
+@st.cache_resource
+def load_model(task):
+    return pipeline(task, model="t5-small", tokenizer="t5-small", framework="pt")
+
+humanizer = load_model("text2text-generation")
+rephraser = load_model("text2text-generation")
+
+# Streamlit App
+st.title("Humanizer & Rephraser App")
+st.subheader("Powered by Hugging Face and Streamlit")
+
+# User Input
+input_text = st.text_area("Enter the text you want to process:", "")
+
+# Options
+task_option = st.radio(
+    "Choose an option:",
+    ("Humanize Text", "Rephrase Text"),
+    index=0
+)
+
+# Optional Settings
+formality = st.slider("Creativity level (lower is more formal):", 0, 100, 50)
+tone = st.selectbox("Select tone:", ["Casual", "Professional", "Neutral"])
+
+# Process Button
+if st.button("Generate Output"):
+    if input_text.strip():
+        with st.spinner("Processing..."):
+            if task_option == "Humanize Text":
+                prompt = f"humanize: {input_text}"
+                result = humanizer(prompt, max_length=128, num_return_sequences=1, temperature=formality / 100)
+            else:
+                prompt = f"rephrase: {input_text}"
+                result = rephraser(prompt, max_length=128, num_return_sequences=1, temperature=formality / 100)
+            
+            st.success("Done!")
+            st.write("**Output:**")
+            st.write(result[0]['generated_text'])
+    else:
+        st.error("Please enter some text to process.")
+
+# Deployment Tip
+st.info("To deploy, run the app using `streamlit run app.py` in your terminal.")