Update app.py

app.py

@@ -143,8 +143,11 @@ 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"""
     
-    if not GROQ_API_KEY:
-        return {"error": "Groq API key not found. Please set the GROQ_API_KEY environment variable."}
+    # Initialize Groq client
+    try:
+        client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+    except Exception as e:
+        return {"error": f"Failed to initialize Groq client: {str(e)}"}
     
     # Format inputs for the API
     prompt = f"""
@@ -179,7 +182,7 @@ def call_groq_api(user_inputs, production_data, cost_data):
     3. Three specific recommendations to reduce costs
     4. An ideal parameter configuration based on the provided inputs
     
-    Format your response as a structured JSON with the following fields:
+    Format your response as a structured JSON with these fields:
     {
         "efficiency_assessment": "text analysis",
         "efficiency_recommendations": ["recommendation1", "recommendation2", "recommendation3"],
@@ -199,38 +202,29 @@ def call_groq_api(user_inputs, production_data, cost_data):
     """
     
     try:
-        # API call to Groq
-        headers = {
-            "Authorization": f"Bearer {GROQ_API_KEY}",
-            "Content-Type": "application/json"
-        }
-        
-        payload = {
-            "messages": [
-                {"role": "user", "content": prompt}
+        # Call Groq API using the client
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
             ],
-            "model": "llama3-70b-8192",
-            "temperature": 0.5,
-            "max_tokens": 1024,
-            "response_format": {"type": "json_object"}
-        }
-        
-        response = requests.post(
-            "https://api.groq.com/openai/v1/chat/completions",
-            headers=headers,
-            json=payload
+            model="llama-3.3-70b-versatile",
+            temperature=0.5,
+            max_tokens=1024,
+            response_format={"type": "json_object"}
         )
         
-        if response.status_code == 200:
-            response_data = response.json()
-            # Extract JSON from the response
-            try:
-                recommendations_json = json.loads(response_data["choices"][0]["message"]["content"])
-                return recommendations_json
-            except json.JSONDecodeError:
-                return {"error": "Failed to parse API response as JSON"}
-        else:
-            return {"error": f"API call failed with status code {response.status_code}: {response.text}"}
+        # Extract the response content
+        response_content = chat_completion.choices[0].message.content
+        
+        # Parse the JSON response
+        try:
+            recommendations_json = json.loads(response_content)
+            return recommendations_json
+        except json.JSONDecodeError:
+            return {"error": "Failed to parse API response as JSON"}
             
     except Exception as e:
         return {"error": f"Error calling Groq API: {str(e)}"}