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| import streamlit as st | |
| import google.generativeai as genai | |
| import hashlib | |
| import random | |
| # Set the Gemini API key | |
| genai.configure(api_key=st.secrets["GEMINI_API_KEY"]) | |
| # Cache responses to avoid redundant API calls | |
| def get_cached_response(key, response=None): | |
| if response: | |
| st.session_state[key] = response | |
| return st.session_state.get(key) | |
| def create_prompt(prompt: str, template: str, tone: str) -> str: | |
| templates = { | |
| "Formal": f"Please make this sound professional and polished:\n\n{prompt}", | |
| "Empathetic": f"Express warmth and empathy:\n\n{prompt}", | |
| "Friendly": f"Make it casual and friendly:\n\n{prompt}", | |
| "Concise": f"Summarize this as clearly as possible:\n\n{prompt}", | |
| "Creative": f"Add a fun, engaging twist:\n\n{prompt}", | |
| "General": f"Make this sound natural and conversational:\n\n{prompt}", | |
| } | |
| custom_prompt = templates.get(template, f"Make this sound natural and conversational:\n\n{prompt}") | |
| tones = { | |
| "Warm": "Use a warm, approachable tone.", | |
| "Confident": "Sound friendly but confident.", | |
| "Apologetic": "Make it sound honest and genuine.", | |
| "Neutral": "Keep it straightforward and clear.", | |
| "Optimistic": "Add a hopeful, upbeat tone.", | |
| "Excited": "Make it energetic and enthusiastic." | |
| } | |
| tone_instruction = tones.get(tone, "Keep it natural and relatable.") | |
| return f"{tone_instruction}\n\n{custom_prompt}" | |
| def refine_text(text: str) -> str: | |
| conversational_inserts = [ | |
| "Honestly,", "Frankly speaking,", "In a nutshell,", "To put it simply,", "If I may add," | |
| ] | |
| replacements = { | |
| "do not": "don't", "cannot": "can't", "will not": "won't", | |
| "it is": "it's", "let us": "let's", "for example": "like," | |
| } | |
| for formal, casual in replacements.items(): | |
| text = text.replace(formal, casual, 1) | |
| if random.random() > 0.5: | |
| insert = random.choice(conversational_inserts) | |
| sentences = text.split(".") | |
| if len(sentences) > 2: | |
| index = random.randint(1, len(sentences) - 2) | |
| sentences.insert(index, insert) | |
| text = ". ".join(sentences).replace("..", ".") | |
| return text.strip() | |
| def generate_text(prompt: str, max_tokens: int, temperature: float) -> str: | |
| try: | |
| model = genai.GenerativeModel("gemini-pro") | |
| response = model.generate_content(prompt, generation_config={ | |
| "temperature": temperature, | |
| "max_output_tokens": max_tokens, | |
| "top_p": 0.9, | |
| "top_k": 40 | |
| }) | |
| if hasattr(response, "text"): | |
| return refine_text(response.text) | |
| else: | |
| st.error("No response text returned from Gemini.") | |
| return None | |
| except Exception as e: | |
| st.error(f"Error generating text: {e}") | |
| return None | |
| def main(): | |
| st.set_page_config(page_title="HumanizeIt", page_icon="📝", layout="wide") | |
| st.title("📝 HumanizeIt") | |
| st.write("Transform your text into something more conversational and human-like.") | |
| st.markdown("### Enter text to humanize:") | |
| prompt = st.text_area("", height=150) | |
| with st.expander("Advanced Options"): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| template = st.selectbox("Choose a Template:", ["General", "Formal", "Friendly", "Empathetic", "Concise", "Creative"]) | |
| max_tokens = st.slider("Max Tokens:", 50, 300, 150) | |
| with col2: | |
| tone = st.selectbox("Select a Tone:", ["Neutral", "Optimistic", "Confident", "Apologetic", "Warm", "Excited"]) | |
| temperature = st.slider("Creativity Level:", 0.1, 1.0, 0.7) | |
| generate_button = st.button("✨ Generate Humanized Text") | |
| if generate_button: | |
| if prompt.strip(): | |
| with st.spinner("Generating..."): | |
| user_prompt = create_prompt(prompt, template, tone) | |
| cache_key = hashlib.md5(user_prompt.encode()).hexdigest() | |
| cached_response = get_cached_response(cache_key) | |
| if cached_response: | |
| st.write("Retrieved from cache.") | |
| humanized_text = cached_response | |
| else: | |
| humanized_text = generate_text(user_prompt, max_tokens, temperature) | |
| if humanized_text: | |
| get_cached_response(cache_key, humanized_text) | |
| if humanized_text: | |
| st.subheader("💡 Humanized Text:") | |
| st.write(humanized_text) | |
| else: | |
| st.warning("Please enter text to humanize.") | |
| st.markdown("---") | |
| st.subheader("Your Feedback Matters!") | |
| feedback = st.radio("Was this helpful?", ["👍 Yes", "👎 No", "😐 Neutral"], horizontal=True) | |
| additional_feedback = st.text_input("Any suggestions or comments?") | |
| if st.button("Submit Feedback"): | |
| st.success("Thank you for your feedback!") | |
| if __name__ == "__main__": | |
| main() | |