updateed the app.py with basic

.gitignore

@@ -0,0 +1,17 @@
+# Ignore Python bytecode files
+__pycache__/
+*.py[cod]
+
+# Ignore environment variables file
+.env
+
+# Ignore log files
+*.log
+
+# Ignore virtual environment directories
+venv/
+env/
+
+# Ignore system files
+.DS_Store
+Thumbs.db 

app.py

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+import streamlit as st
+import os
+from groq import Groq
+from dotenv import load_dotenv
+import logging
+
+# Configure logging
+logging.basicConfig(
+    filename='app.log',
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+# Test logging
+logging.debug("Logging is configured correctly and this is a test message.")
+
+# Load environment variables
+load_dotenv()
+reflection_cycles = 2
+
+# Define the Groq API key and initialize the client
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    raise ValueError("API Key is not set. Please check your environment variables or .env file.")
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# Define the Reflexion system prompt with formatted user variables
+SYSTEM_PROMPT = (
+    "You are an advanced AI agent leveraging the Reflexion framework to iteratively improve ideas and responses through up to {reflection_cycles} cycles of reflection. "
+    "Your goal is to provide the most meaningful, relevant, and impactful results while autonomously managing the process. Follow the structured workflow below:\n\n"
+    "Instructions:\n\n"
+    "Initial Response:\n"
+    "Begin with the {user_input} and provide a well-considered, thoughtful initial answer. Ensure clarity, relevance, and creativity in addressing the query.\n\n"
+    "Reflection Cycles (Up to {reflection_cycles}):\n"
+    "After each response, perform a critical reflection, considering the following:\n"
+    "Alignment: Does the answer align with the user's intent?\n"
+    "Feasibility: Are the ideas or solutions practical and actionable?\n"
+    "Depth: Are there gaps, ambiguities, or missed perspectives?\n"
+    "Impact: How meaningful and beneficial is the response to the user?\n"
+    "Use the feedback from this reflection to refine the response, document insights in a temporary memory buffer, and iterate the process.\n\n"
+    "Continue refining and evolving the response for up to {reflection_cycles} cycles or until you reach a well-optimized conclusion.\n\n"
+    "Episodic Memory Storage:\n"
+    "Maintain a temporary memory buffer to track reflections and refinements. Use this to avoid redundant steps and ensure improvements are based on accumulated insights.\n\n"
+    "Final Output:\n"
+    "Deliver a final, polished answer that is thoughtful, comprehensive, and fully addresses the user's query.\n"
+    "Provide a summary of the ideation journey, highlighting key outcomes and how the response evolved over the iterations."
+).format(reflection_cycles=reflection_cycles, user_input="{user_input}")
+
+# Initialize Streamlit app
+st.title("Reflexion AI Agent")
+
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Function to summarize user input if necessary
+def summarize_input(user_input):
+    return user_input
+
+# Function to generate responses using the Groq API
+def generate_response(user_input, reflection_memory):
+    try:
+        combined_length = len(SYSTEM_PROMPT) + len(user_input)
+        reflection_memory_content = [msg["content"] for msg in reflection_memory[-3:]]
+        reflection_memory_length = len(" ".join(reflection_memory_content))
+        context_limit = 8192
+
+        logging.debug(f"Combined length of system prompt and user input: {combined_length}")
+        logging.debug(f"Reflection memory length: {reflection_memory_length}")
+
+        if combined_length + reflection_memory_length > context_limit:
+            user_input = summarize_input(user_input)
+            combined_length = len(SYSTEM_PROMPT) + len(user_input)
+            logging.debug(f"Summarized input length: {len(user_input)}")
+            logging.debug(f"New combined length: {combined_length + reflection_memory_length}")
+
+        chat_completion = client.chat.completions.create(
+            model="llama3-8b-8192",
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": user_input},
+                {"role": "assistant", "content": " ".join(reflection_memory_content)}
+            ],
+            max_tokens=1024,
+            temperature=0.6,
+            top_p=0.9,
+        )
+
+        logging.debug(f"Full API Response: {chat_completion}")
+
+        if chat_completion.choices and len(chat_completion.choices) > 0:
+            content = chat_completion.choices[0].message.content
+            if content:
+                # Extract the "Final Output" section if it exists, otherwise use the entire content
+                final_output_start = content.find("Final Output:")
+                if final_output_start != -1:
+                    final_output = content[final_output_start:].strip()
+                else:
+                    final_output = content.strip()
+                return final_output
+            else:
+                logging.warning("Received empty content in API response.")
+                return None
+        else:
+            raise ValueError("Invalid response format: No choices found.")
+
+    except Exception as e:
+        logging.error(f"Error generating response: {e}", exc_info=True)
+        st.error(f"Error generating response: {e}")
+        return None
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if user_input := st.chat_input("You: "):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(user_input)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": user_input})
+
+    # Generate and display assistant response
+    last_valid_response = None
+    for _ in range(reflection_cycles):
+        bot_response = generate_response(user_input, st.session_state.messages)
+        if bot_response:
+            last_valid_response = bot_response
+
+    # Display only the final output
+    if last_valid_response:
+        st.chat_message("assistant").markdown(last_valid_response)
+        st.session_state.messages.append({"role": "assistant", "content": last_valid_response})

requirements.txt

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+   streamlit
+   requests
+   python-dotenv
+   groq