update app.py

app.py

@@ -1,7 +1,66 @@
+# import streamlit as st
+# import random
+# import time
+# import google.generativeai as genai
+
+# # API Key
+# GOOGLE_API_KEY = 'AIzaSyDnvz-l-Hq_tNkRkx2j_W8pd41XT08nH6Y'
+# genai.configure(api_key=GOOGLE_API_KEY)
+
+# # Create a model object
+# model = genai.GenerativeModel('gemini-pro')
+
+# # Streamlit app layout
+# def main():
+#     st.title("Prompt Pilot")
+
+#     # Sidebar for prompt format selection
+#     st.sidebar.title("Select Prompt Format")
+#     prompt_format = st.sidebar.radio("Options", ("RISE", "RTF"))
+#     st.sidebar.write(f"Selected Format: {prompt_format}")
+
+#     # Chat section
+#     st.header("Chat Section")
+
+#     # Initialize chat history
+#     if "messages" not in st.session_state:
+#         st.session_state.messages = []
+
+#     # Accept user input
+#     if prompt := st.chat_input("Enter your prompt:"):
+#         # Clear previous messages
+#         st.session_state.messages = []
+
+#         # Add user message to chat history
+#         st.session_state.messages.append({"role": "user", "content": prompt})
+        
+#         # Generate the query for the Gemini model
+#         query = f"You are a prompt engineering assistant. Your task is to transform a raw prompt into an enhanced format, specifically using either RTF (Role Task Format) or RISE (Role Input Steps Expectations), as specified. The output should follow the {prompt_format} structure, and must not include any answers, hints, or suggestions, choose the role efficiently and critically related to that field. The goal is to prepare a prompt that, when used by a generative model, will produce the most accurate and relevant response. Here is the user's raw prompt: '{prompt}'. Please convert it into the {prompt_format} format."
+
+#         # Display user message in chat message container
+#         with st.chat_message("user"):
+#             st.markdown(prompt)
+
+#         # Generate response using the Gemini model
+#         assistant_response = model.generate_content(query)
+
+#         # Display assistant response in chat message container
+#         with st.chat_message("assistant"):
+#             st.markdown(assistant_response.text)
+
+#         # Add assistant response to chat history
+#         st.session_state.messages.append({"role": "assistant", "content": assistant_response.text})
+
+# if __name__ == "__main__":
+#     main()
+
+
+
+
 import streamlit as st
-import random
-import time
 import google.generativeai as genai
+import json
+import os
 
 # API Key
 GOOGLE_API_KEY = 'AIzaSyDnvz-l-Hq_tNkRkx2j_W8pd41XT08nH6Y'
@@ -10,6 +69,30 @@ genai.configure(api_key=GOOGLE_API_KEY)
 # Create a model object
 model = genai.GenerativeModel('gemini-pro')
 
+# Function to save prompts to a JSON file
+def save_prompts_to_json(prompt_format, raw_prompt, model_prompt):
+    # Save only if the selected format matches
+    data = {
+        "raw_prompt": raw_prompt,
+        "model_prompt": model_prompt,
+        "prompt_format": prompt_format
+    }
+
+    # If file exists, append to it; otherwise, create a new file
+    file_path = '/content/prompts.json'  # Adjusted to save in the current directory
+    if os.path.exists(file_path):
+        with open(file_path, 'r+') as file:
+            # Load existing data
+            existing_data = json.load(file)
+            # Append new data
+            existing_data.append(data)
+            # Write updated data back to the file
+            file.seek(0)
+            json.dump(existing_data, file, indent=4)
+    else:
+        with open(file_path, 'w') as file:
+            json.dump([data], file, indent=4)
+
 # Streamlit app layout
 def main():
     st.title("Prompt Pilot")
@@ -33,7 +116,7 @@ def main():
 
         # Add user message to chat history
         st.session_state.messages.append({"role": "user", "content": prompt})
-        
+
         # Generate the query for the Gemini model
         query = f"You are a prompt engineering assistant. Your task is to transform a raw prompt into an enhanced format, specifically using either RTF (Role Task Format) or RISE (Role Input Steps Expectations), as specified. The output should follow the {prompt_format} structure, and must not include any answers, hints, or suggestions, choose the role efficiently and critically related to that field. The goal is to prepare a prompt that, when used by a generative model, will produce the most accurate and relevant response. Here is the user's raw prompt: '{prompt}'. Please convert it into the {prompt_format} format."
 
@@ -43,13 +126,17 @@ def main():
 
         # Generate response using the Gemini model
         assistant_response = model.generate_content(query)
+        model_prompt = assistant_response.candidates[0].content.parts[0].text
 
         # Display assistant response in chat message container
         with st.chat_message("assistant"):
-            st.markdown(assistant_response.text)
+            st.markdown(model_prompt)
 
         # Add assistant response to chat history
-        st.session_state.messages.append({"role": "assistant", "content": assistant_response.text})
+        st.session_state.messages.append({"role": "assistant", "content": model_prompt})
+
+        # Save raw prompt and model-generated prompt to a JSON file in the selected format
+        save_prompts_to_json(prompt_format, prompt, model_prompt)
 
 if __name__ == "__main__":
     main()