Create app.py

app.py

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+import streamlit as st
+import os
+import google.generativeai as genai
+from dotenv import load_dotenv
+import json
+
+# Load environment variables
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+# Configure Generative AI model
+if GOOGLE_API_KEY:
+    genai.configure(api_key=GOOGLE_API_KEY)
+    model = genai.GenerativeModel('gemini-pro')  # You can experiment with other available models
+else:
+    st.error("Google AI Studio API key not found. Please add it to your .env file.")
+    st.stop()
+
+st.title("Prompt Engineering Playground")
+st.subheader("Experiment with Fundamental Prompting Techniques")
+
+with st.sidebar:
+    st.header("Prompting Concepts")
+    st.markdown(
+        """
+        This app demonstrates fundamental prompt engineering techniques based on the 
+        Google Generative AI course.
+        """
+    )
+    st.subheader("Key Techniques:")
+    st.markdown(
+        """
+        -   **Clear and Specific Instructions**: Providing explicit guidance to the model.
+        -   **Using Delimiters**: Clearly separating different parts of the input text.
+        -   **Asking for Structured Output**: Requesting output in a specific format (e.g., JSON).
+        -   **Checking Assumptions**: Verifying if certain conditions are met.
+        -   **Providing Examples (Few-Shot Prompting)**: Giving the model a few examples of the desired input-output behavior.
+        -   **Temperature Control**: Adjusting the randomness of the model's output.
+        -   **Chain-of-Thought Prompting**: Encouraging the model to show its reasoning process.
+        """
+    )
+    st.subheader("Whitepaper Insights:")
+    st.markdown(
+        """
+        -   Understanding LLM capabilities and limitations.
+        -   Importance of prompt clarity and specificity.
+        -   Iterative prompt development and refinement.
+        -    Context window awareness
+        """
+    )
+
+# --- Prompting Techniques Section ---
+st.header("Experiment with Prompts")
+
+prompt_technique = st.selectbox(
+    "Choose a Prompting Technique to Try:",
+    [
+        "Simple Instruction",
+        "Using Delimiters",
+        "Requesting JSON Output",
+        "Checking Assumptions",
+        "Providing Examples (Few-Shot)",
+        "Temperature Control",
+        "Chain-of-Thought Prompting"
+    ],
+    index=0  # Start with "Simple Instruction"
+)
+
+prompt_input = st.text_area("Enter your prompt here:", height=150)
+
+# Temperature slider (common to several techniques)
+temperature = st.slider(
+    "Temperature:",
+    min_value=0.0,
+    max_value=1.0,
+    value=0.7,  # Default temperature
+    step=0.01,
+    help="Controls the randomness of the output. Lower values are more deterministic; higher values are more creative.",
+)
+
+if st.button("Generate Response"):
+    if not prompt_input:
+        st.warning("Please enter a prompt.")
+    else:
+        with st.spinner("Generating..."):
+            try:
+                if prompt_technique == "Using Delimiters":
+                    delimiter = st.text_input("Enter your delimiter (e.g., ###, ---):", "###")
+                    processed_prompt = f"Here is the input, with parts separated by '{delimiter}':\n{prompt_input}\n Please process each part separately."
+                    response = model.generate_content(
+                        processed_prompt, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    st.subheader("Generated Response:")
+                    st.markdown(response.text)
+
+                elif prompt_technique == "Requesting JSON Output":
+                    json_format = st.text_input(
+                        "Describe the desired JSON format (e.g.,  {'name': str, 'age': int}):", "{'key1': type, 'key2': type}"
+                    )
+                    processed_prompt = f"Please provide the output in JSON format, following this structure: {json_format}.  Here is the information: {prompt_input}"
+                    response = model.generate_content(
+                        processed_prompt, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    try:
+                        json_output = json.loads(response.text)
+                        st.subheader("Generated JSON Output:")
+                        st.json(json_output)
+                    except json.JSONDecodeError:
+                        st.error("Failed to decode JSON. Raw response:")
+                        st.text(response.text)
+
+                elif prompt_technique == "Checking Assumptions":
+                    assumption = st.text_input("State the assumption you want the model to check:", "The main subject is a person")
+                    processed_prompt = f"First, check if the following assumption is true: '{assumption}'.  Then, answer the prompt: {prompt_input}"
+                    response = model.generate_content(
+                        processed_prompt, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    st.subheader("Generated Response:")
+                    st.markdown(response.text)
+
+                elif prompt_technique == "Providing Examples (Few-Shot)":
+                    example1_input = st.text_area("Example 1 Input:", height=50)
+                    example1_output = st.text_area("Example 1 Output:", height=50)
+                    example2_input = st.text_area("Example 2 Input (Optional):", height=50)
+                    example2_output = st.text_area("Example 2 Output (Optional):", height=50)
+
+                    processed_prompt = "Here are some examples:\n"
+                    processed_prompt += f"Input: {example1_input}\nOutput: {example1_output}\n"
+                    if example2_input and example2_output:
+                        processed_prompt += f"Input: {example2_input}\nOutput: {example2_output}\n"
+                    processed_prompt += f"\nNow, answer the following:\nInput: {prompt_input}"
+
+                    response = model.generate_content(
+                        processed_prompt, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    st.subheader("Generated Response:")
+                    st.markdown(response.text)
+
+                elif prompt_technique == "Temperature Control":
+                    # The temperature slider is already handled, so we just pass it to the model
+                    response = model.generate_content(
+                        prompt_input, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    st.subheader("Generated Response:")
+                    st.markdown(response.text)
+                
+                elif prompt_technique == "Chain-of-Thought Prompting":
+                    cot_prompt = f"Let's think step by step. {prompt_input}"
+                    response = model.generate_content(cot_prompt, generation_config=genai.types.GenerationConfig(temperature=temperature))
+                    st.subheader("Generated Response (Chain-of-Thought):")
+                    st.markdown(response.text)
+
+                else:  # Simple Instruction
+                    response = model.generate_content(
+                        prompt_input, generation_config=genai.types.GenerationConfig(temperature=temperature)
+                    )
+                    st.subheader("Generated Response:")
+                    st.markdown(response.text)
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")