Update app.py

app.py

@@ -10,7 +10,7 @@ hf_token = os.getenv('HF_API_TOKEN')
 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
+
 
 
 # Define your prompt template
@@ -46,12 +46,12 @@ def format_prompt(description, columns):
 import requests
 
 # Define your Streamlit Space inference URL
-inference_endpoint = "https://yakine-model.hf.space/predict"
+inference_endpoint = "https://huggingface.co/spaces/yakine/model"  # Update this to your Streamlit Space URL
 
 def generate_synthetic_data(description, columns):
     try:
-        # Format the prompt
-        formatted_prompt = format_prompt(description, columns)
+        # Format the prompt for your Llama 3 model
+        formatted_prompt = f"{description}, with columns: {', '.join(columns)}"  # Adjust this based on your Streamlit app's prompt format
         
         # Send a POST request to the Streamlit Space API
         headers = {
@@ -59,7 +59,7 @@ def generate_synthetic_data(description, columns):
             "Content-Type": "application/json"
         }
         data = {
-            "inputs": formatted_prompt,
+            "inputs": formatted_prompt,  # Adjust according to the input expected by your Streamlit app
             "parameters": {
                 "max_new_tokens": 512,
                 "top_p": 0.90,
@@ -67,13 +67,13 @@ def generate_synthetic_data(description, columns):
             }
         }
         
-        response = requests.post(inference_endpoint, json=data, headers=headers)
+        response = requests.post(inference_endpoint + "/predict", json=data, headers=headers)
 
         if response.status_code != 200:
             return f"Error: {response.status_code}, {response.text}"
         
         # Extract the generated text from the response
-        generated_text = response.json()['data']  # Adjust based on your Streamlit Space response structure
+        generated_text = response.json().get('data')  # Adjust based on your Streamlit Space response structure
         return generated_text
 
     except Exception as e: