Update app.py

app.py

@@ -11,7 +11,7 @@ if not hf_token:
     raise ValueError("Hugging Face API token is not set. Please set the HF_API_TOKEN environment variable.")
 
 # Set the inference endpoint URL
-inference_endpoint = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3.1-8B"
+
 
 # Define your prompt template
 prompt_template = """\
@@ -43,12 +43,17 @@ def format_prompt(description, columns):
     prompt = prompt_template.format(description=processed_description, columns=",".join(columns))
     return prompt
 
+import requests
+
+# Define your Streamlit Space inference URL
+inference_endpoint = "https://yakine-llama31.hf.space/predict"
+
 def generate_synthetic_data(description, columns):
     try:
         # Format the prompt
         formatted_prompt = format_prompt(description, columns)
         
-        # Send a POST request to the Hugging Face Inference API
+        # Send a POST request to the Streamlit Space API
         headers = {
             "Authorization": f"Bearer {hf_token}",
             "Content-Type": "application/json"
@@ -68,13 +73,14 @@ def generate_synthetic_data(description, columns):
             return f"Error: {response.status_code}, {response.text}"
         
         # Extract the generated text from the response
-        generated_text = response.json()[0]['generated_text']
+        generated_text = response.json()['data']  # Adjust based on your Streamlit Space response structure
         return generated_text
 
     except Exception as e:
         print(f"Error in generate_synthetic_data: {e}")
         return f"Error: {e}"
 
+
 def generate_large_synthetic_data(description, columns, num_rows=1000, rows_per_generation=100):
     data_frames = []
     num_iterations = num_rows // rows_per_generation