Create app.py

app.py

@@ -0,0 +1,35 @@
+# app.py
+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "mradermacher/Indian_Legal_Assistant-GGUF"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModel.from_pretrained(model_name)
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Streamlit App Layout
+st.title("Indian Legal Assistant - Hugging Face Spaces Deployment")
+st.write("This app provides answers to legal questions using the Indian Legal Assistant model.")
+
+# User input for a legal query
+user_input = st.text_area("Enter your legal question:")
+
+if st.button("Generate Response"):
+    if user_input:
+        # Tokenize the input
+        inputs = tokenizer(user_input, return_tensors="pt")
+
+        # Generate response
+        outputs = model.generate(**inputs, max_length=150)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Display the response
+        st.write("### Response:")
+        st.write(response)
+    else:
+        st.write("Please enter a question to get a response.")