Update app.py

app.py

@@ -6,24 +6,39 @@ import gradio as gr
 # Configure the Gemini API
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
 
-# Generation configuration
-generation_config = {
-    "temperature": 1,
-    "top_p": 0.95,
-    "top_k": 64,
-    "max_output_tokens": 8192,
-    "response_mime_type": "text/plain",
+# Define the system instruction (pre_prompt)
+pre_prompt = """
+You are a chemistry expert. Break down the given chemical reaction mechanism into simple steps. 
+The output should be in JSON format with the following structure:
+
+{
+    "step 1": {
+        "reactants": ["reactant 1", "reactant 2"],
+        "products": ["product 1", "product 2"],
+        "reason": "Describe the reason or logic behind this step",
+        "reagent": "Optional reagent or conditions for this step"
+    },
+    "step 2": { ... }
+    ...
 }
+"""
 
-# Define the model
+# Define the model with the system instruction (pre_prompt)
 model = genai.GenerativeModel(
     model_name="gemini-1.5-flash",
-    generation_config=generation_config,
+    system_instruction=pre_prompt,
+    generation_config={
+        "temperature": 1,
+        "top_p": 0.95,
+        "top_k": 64,
+        "max_output_tokens": 8192,
+        "response_mime_type": "text/plain",
+    },
 )
 
 # Function to generate steps for A -> D flow
 def generate_reaction_steps(reactants, products):
-    prompt = f"Given reactants: {reactants} and products: {products}, break down the reaction mechanism into simple steps and provide in JSON format."
+    prompt = f"Given reactants: {reactants} and products: {products}, break down the reaction mechanism into simple steps in JSON format."
     
     chat_session = model.start_chat(history=[])
     response = chat_session.send_message(prompt)