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import streamlit as st
import openai
import anthropic
import pandas as pd
import numpy as np
import time
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
import os
# Initialize clients
openai.api_key = os.getenv("OPENAI_API_KEY")
anthropic_client = anthropic.Client(api_key=os.getenv("ANTHROPIC_API_KEY"))
# Load sentence transformer for similarity scoring
similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
# ===== Helper Functions =====
def generate_response(prompt, model):
"""Generate responses from different models."""
if model == "GPT-4":
response = openai.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content.strip()
elif model == "Claude 3":
response = anthropic_client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=1000,
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
else:
return "Model not supported."
def calculate_similarity(text1, text2):
"""Calculate cosine similarity between two texts."""
embeddings = similarity_model.encode([text1, text2])
return cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
def evaluate_response(response, golden_response):
"""Evaluate response quality."""
similarity = calculate_similarity(response, golden_response)
return {
"Similarity Score": similarity,
"Length": len(response),
"Toxicity": "Low" # Placeholder for actual toxicity detection
}
def optimize_prompt(prompt, target_metrics):
"""Optimize prompt using a simple mutation strategy."""
# Placeholder for genetic algorithm implementation
optimized_prompt = prompt + " [Optimized for clarity and brevity]"
return optimized_prompt
# ===== Streamlit UI =====
st.title("🚀 PromptCraft Studio")
st.write("The Ultimate Prompt Engineering Workbench")
# Sidebar for settings
with st.sidebar:
st.header("⚙️ Settings")
model_choice = st.selectbox("Choose Model", ["GPT-4", "Claude 3"])
optimization_goal = st.selectbox("Optimization Goal", ["Clarity", "Brevity", "Engagement"])
# Main interface
st.markdown("### **Prompt Playground**")
user_prompt = st.text_area("Enter your prompt:", "Explain quantum computing in simple terms.")
if st.button("Generate Response"):
with st.spinner("Generating response..."):
response = generate_response(user_prompt, model_choice)
st.markdown("### **Response**")
st.write(response)
# Evaluate response
st.markdown("### **Evaluation Metrics**")
golden_response = "Quantum computing uses qubits to perform complex calculations faster than classical computers."
metrics = evaluate_response(response, golden_response)
st.write(metrics)
# Prompt Optimization
st.markdown("### **Prompt Optimization**")
if st.button("Optimize Prompt"):
optimized_prompt = optimize_prompt(user_prompt, optimization_goal)
st.markdown("### **Optimized Prompt**")
st.write(optimized_prompt)
# Multi-Model Comparison
st.markdown("### **Multi-Model Comparison**")
if st.button("Compare Models"):
col1, col2 = st.columns(2)
with col1:
st.markdown("#### **GPT-4 Response**")
gpt_response = generate_response(user_prompt, "GPT-4")
st.write(gpt_response)
with col2:
st.markdown("#### **Claude 3 Response**")
claude_response = generate_response(user_prompt, "Claude 3")
st.write(claude_response)
# Compare similarity
similarity = calculate_similarity(gpt_response, claude_response)
st.markdown(f"**Similarity Between Models:** {similarity:.2f}")
# Bulk Processing
st.markdown("### **Bulk Prompt Testing**")
uploaded_file = st.file_uploader("Upload a CSV with prompts", type=["csv"])
if uploaded_file:
df = pd.read_csv(uploaded_file)
if "Prompt" in df.columns:
results = []
for prompt in df["Prompt"]:
response = generate_response(prompt, model_choice)
results.append(response)
df["Response"] = results
st.write(df)
st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "results.csv", "text/csv")
else:
st.error("CSV must contain a 'Prompt' column.")
# Feedback
st.markdown("### **Feedback**")
if st.button("👍 Good Response"):
st.toast("Thanks for your feedback!")
if st.button("👎 Needs Improvement"):
st.toast("We'll do better next time!") |