Update app.py

app.py

@@ -1,103 +1,17 @@
+import gradio as gr
 import requests
 import os
-import json
-import gradio as gr
-import logging
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
 
-# Hugging Face API setup
 HF_API_TOKEN = os.getenv("HF_API_TOKEN")
-HF_API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
-hf_headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-
-# SAP configuration
-SAP_API_KEY = os.getenv('SAP_API_KEY')
-SAP_BASE_URL = "https://sandbox.api.sap.com/s4hanacloud/sap/opu/odata/sap"
-sap_headers = {
-    "APIKey": SAP_API_KEY,
-    "Accept": "application/json"
-}
-
-def fetch_sales_orders(top=20):
-    url = f"{SAP_BASE_URL}/API_SALES_ORDER_SRV/A_SalesOrder?$top={top}"
-    response = requests.get(url, headers=sap_headers)
-    return response.json().get('d', {}).get('results', [])
-
-def fetch_purchase_orders(top=20):
-    url = f"{SAP_BASE_URL}/API_PURCHASEORDER_PROCESS_SRV/A_PurchaseOrder?$top={top}"
-    response = requests.get(url, headers=sap_headers)
-    return response.json().get('d', {}).get('results', [])
-
-def generate_response(question, context, data_type):
-    context_str = json.dumps(context, indent=2)
-    if len(context_str) > 4000:  # Limit context size
-        context_str = context_str[:4000] + "... (truncated)"
-
-    prompt = f"""
-    You are a helpful SAP data analyst. Answer the user's question based on the provided data.
-
-    Data Type: {data_type}
-
-    Available Data:
-    {context_str}
-
-    User Question: {question}
-
-    Instructions:
-    1. Provide a clear, concise answer based on the data.
-    2. Include specific numbers, dates, or values when relevant.
-    3. If the data doesn't contain enough information, mention this.
-    4. Format your response clearly and concisely.
-
-    Answer:
-    """
-
-    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 500, "temperature": 0.1}}
-    response = requests.post(HF_API_URL, headers=hf_headers, json=payload)
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 
+def call_llama3(prompt):
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
     if response.status_code == 200:
-        output = response.json()
-        generated_text = output[0]['generated_text']
-        return generated_text.replace(prompt, "").strip()
+        return response.json()[0]['generated_text']
     else:
-        logger.error(f"Hugging Face API Error: {response.status_code}, {response.text}")
-        return "Error generating response from Hugging Face."
-
-def chat_with_sap(question, history):
-    sales_orders = fetch_sales_orders()
-    purchase_orders = fetch_purchase_orders()
-
-    context = {"sales_orders": sales_orders, "purchase_orders": purchase_orders}
-    data_type = "Sales and Purchase Orders"
-
-    response = generate_response(question, context, data_type)
-    history.append((question, response))
-    return history
-
-# Gradio UI
-with gr.Blocks(title="SAP Order Analytics with LLAMA-3") as demo:
-    gr.Markdown("""
-    # 🚀 SAP Order Analytics Agent (LLAMA-3)
-
-    This agent analyzes SAP Sales and Purchase Orders. Example queries:
-    - "List recent sales orders"
-    - "Total net amount of purchase orders"
-    - "Sales orders for a specific customer"
-    """)
-
-    chatbot = gr.Chatbot(height=500)
-    msg = gr.Textbox(label="Your Question", placeholder="Ask your SAP query here...")
-    submit_btn = gr.Button("Send", variant="primary")
-    clear_btn = gr.Button("Clear")
-
-    submit_btn.click(chat_with_sap, [msg, chatbot], [chatbot])
-    msg.submit(chat_with_sap, [msg, chatbot], [chatbot])
-    clear_btn.click(lambda: [], None, chatbot)
-    submit_btn.click(lambda: "", None, msg)
-    msg.submit(lambda: "", None, msg)
+        return f"Error: {response.status_code} - {response.text}"
 
-if __name__ == "__main__":
-    demo.launch(debug=True)
+demo = gr.Interface(call_llama3, "text", "text")
+demo.launch()