Update app.py

app.py

@@ -142,15 +142,15 @@ def calculate_cost(method, water_cost, water_purification_cost, energy_source, e
 
 def call_groq_api(user_inputs, production_data, cost_data):
     """Call Groq API with Llama 3 to analyze production parameters and provide recommendations"""
-    
+        
     # Initialize Groq client
     try:
-        client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+        client = Groq(api_key=os.environ.get("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"))
     except Exception as e:
         return {"error": f"Failed to initialize Groq client: {str(e)}"}
     
-    # Format inputs for the API
-    prompt = f"""
+    # Prepare the prompt without f-string for the JSON schema part
+    prompt_part1 = f"""
     As a hydrogen production expert, analyze the following electrolysis parameters and provide recommendations for optimization:
     
     Input Parameters:
@@ -183,6 +183,10 @@ def call_groq_api(user_inputs, production_data, cost_data):
     4. An ideal parameter configuration based on the provided inputs
     
     Format your response as a structured JSON with these fields:
+    """
+    
+    # The JSON schema part without using f-string
+    prompt_part2 = """
     {
         "efficiency_assessment": "text analysis",
         "efficiency_recommendations": ["recommendation1", "recommendation2", "recommendation3"],
@@ -201,6 +205,9 @@ def call_groq_api(user_inputs, production_data, cost_data):
     }
     """
     
+    # Combine the parts
+    prompt = prompt_part1 + prompt_part2
+    
     try:
         # Call Groq API using the client
         chat_completion = client.chat.completions.create(