Create app.py

app.py

@@ -0,0 +1,32 @@
+import streamlit as st
+import os
+import requests
+
+# Set your model and project details
+project_name = 'my_autotrain_llm'
+model_name = 'abhishek/llama-2-7b-hf-small-shards'
+hf_token = 'YOUR_HUGGING_FACE_TOKEN'  # Replace with your Hugging Face token
+
+# Set Hugging Face API endpoint
+hf_api_endpoint = 'https://huggingface.co/am-nandeesh/jql'
+
+st.title("Streamlit App for Hugging Face Spaces")
+
+user_input = st.text_area("Enter Text:", "Type your text here...")
+
+
+if st.button("Run Model"):
+    headers = {"Authorization": f"Bearer {hf_token}"}
+    data = {"inputs": user_input}
+
+    response = requests.post(
+        f"{hf_api_endpoint}{model_name}/tasks/text-generation",
+        headers=headers,
+        json=data
+    )
+
+    if response.status_code == 200:
+        output_text = response.json()['predictions'][0]
+        st.success(f"Model Output: {output_text}")
+    else:
+        st.error(f"Error running the model. Status code: {response.status_code}")