import streamlit as st from openai import OpenAI from script_prompts import SYSTEM_MESSAGE_TEXT, SYSTEM_MESSAGE_TH_VO, USER_MESSAGE_TEXT_VO from prompts import SYSTEM_MESSAGE, USER_MESSAGE import json import os import time import requests from bs4 import BeautifulSoup PERSPECTIVE_INSTRUCTIONS = { "auto": 'The script should match the perspective (first person, third person, etc) of the reference material.', "first": 'The script should be written in first person perspective.', "third": 'The script should be written in third person perspective.', } DEFAULT_TONE_INSTRUCTION = 'the same as the tone of the reference material' DEFAULT_TONE = 'balanced' def fetch_blog_content(url): try: res = requests.get(url, timeout=10) res.raise_for_status() # Raise an error for bad status codes soup = BeautifulSoup(res.text, "html.parser") # A simple extraction: get all text within

tags paragraphs = soup.find_all('p') content = "\n\n".join([p.get_text() for p in paragraphs]) return content.strip() except Exception as e: st.error(f"Error fetching blog content: {e}") return "" # Set Streamlit layout to wide mode st.set_page_config(layout="wide") st.title("🎬 AI-Powered Content Planner & Script Writer - Public Blueprints") st.markdown("Paste a source content for generating a Public Blueprints Content Plan and Scripts.") # Placeholder at the very top for response time plan_time_placeholder = st.empty() script_time_placeholder = st.empty() # Models to try OPENAI_MODELS = ["gpt-4o", "gpt-4o-mini", "o3-mini"] GROQ_MODELS = ["llama-3.3-70b-specdec", "llama-3.3-70b-versatile", "llama-3.1-8b-instant", "deepseek-r1-distill-llama-70b"] # Sidebar: Model Selection st.sidebar.subheader("πŸ“€ Model for Plan Generation") blueprint_plan_model = st.sidebar.selectbox( "Choose model for blueprints content plan:", GROQ_MODELS + OPENAI_MODELS, index=0 # Default selection ) st.sidebar.subheader("πŸ“₯ Model for Script Writing") script_writing_model = st.sidebar.selectbox( "Choose model for script writing:", GROQ_MODELS + OPENAI_MODELS, index=0 # Default selection ) # Assign the correct URL based on the selected model if blueprint_plan_model in GROQ_MODELS: plan_client = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.environ.get("GROQ_API_KEY")) else: plan_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY")) if script_writing_model in GROQ_MODELS: script_client = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.environ.get("GROQ_API_KEY")) else: script_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY")) # Layout: Two columns - left for reference document, right for plans and scripts col_doctext, col_output = st.columns([1, 1]) # Left Column: Input Source with col_doctext: st.subheader("πŸ“ Input Source") blog_url = st.text_input("Enter a blog URL (optional):", key="blog_url") # Initialize fetched_content as empty string. fetched_content = "" if blog_url: fetched_content = fetch_blog_content(blog_url) if fetched_content: st.success("Blog content fetched successfully!") else: st.warning("Unable to fetch blog content. Please paste your content manually.") # The text area uses fetched_content as its default value. doctext = st.text_area("Or paste your content here:", value=fetched_content, height=400, key="doctext") # Right Column: Plan Generation and Script Writing with col_output: st.subheader("πŸ“‹ Generated Content Plans") time_placeholder = st.empty() # Button to generate clip plans from the transcript if st.button("Generate Plan"): if not doctext.strip(): st.error("❌ Please enter an input source content.") else: with st.spinner("⏳ Generating content plan... Please wait."): try: # Prepare prompts for clip plan generation system_prompt = SYSTEM_MESSAGE user_prompt = USER_MESSAGE.format(source_content=doctext) messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ] openai_args = { "model": blueprint_plan_model, "messages": messages, "response_format": {"type": "json_object"}, } if blueprint_plan_model == "o3-mini": openai_args["reasoning_effort"] = "low" else: openai_args["max_tokens"] = 5000 openai_args["temperature"] = 0.45 start_time = time.time() response = plan_client.chat.completions.create(**openai_args) end_time = time.time() elapsed_time = end_time - start_time plan_time_placeholder.markdown(f"⏱️ **Response Time for Content Planning:** {elapsed_time:.2f} seconds") generated_response = response.choices[0].message.content.strip() content_plan = json.loads(generated_response) # Assume the response JSON has a single key containing a list of clip plans plan_key = list(content_plan.keys())[0] blueprint_plans = content_plan.get(plan_key, []) # Save clip plans in session state so they persist st.session_state.blueprint_plans = blueprint_plans # Clear any previous extraction outputs for i in range(len(blueprint_plans)): st.session_state.pop(f"extracted_blueprint_{i}", None) except json.JSONDecodeError: st.error("⚠️ Failed to parse OpenAI response. Try again.") except Exception as e: st.error(f"❌ Error: {str(e)}") # Display clip plans if they exist in session state if "blueprint_plans" in st.session_state: # We'll work with a reference to the clip plans list updated_blueprint_plans = st.session_state.blueprint_plans for i, blueprint in enumerate(updated_blueprint_plans): # Each blueprint is rendered in an expander with editable fields with st.expander(f"🎬 Blueprint Plan {i + 1}", expanded=True): st.markdown(f"**Blueprint:** {blueprint.get('Blueprint', 'N/A')}") st.markdown(f"**Content Focus:** {blueprint.get('Content Focus', 'N/A')}") st.markdown(f"**Narrative Thread:** {blueprint.get('Narrative Thread', 'N/A')}") st.markdown(f"**Tag:** {blueprint.get('Tag', 'N/A')}") # Button for transcript extraction for this clip if st.button("Generate Script", key=f"extract_{i}"): with st.spinner("⏳ Generating script... Please wait."): try: # Send only the specific (and possibly edited) content focus to the script writer content_focus_script = updated_blueprint_plans[i].get("Content Focus", "N/A") script_user_prompt = USER_MESSAGE_TEXT_VO.format( doctext=doctext, content_focus=content_focus_script ) if updated_blueprint_plans[i].get("Narrative Thread") == 'text': script_system_prompt = SYSTEM_MESSAGE_TEXT.format( perspective_instruction=PERSPECTIVE_INSTRUCTIONS.get("auto"), tone_instruction=DEFAULT_TONE_INSTRUCTION, ) else: script_system_prompt = SYSTEM_MESSAGE_TH_VO.format( perspective_instruction=PERSPECTIVE_INSTRUCTIONS.get("auto"), tone_instruction=DEFAULT_TONE_INSTRUCTION, ) clipper_messages = [ {"role": "system", "content": script_system_prompt}, {"role": "user", "content": script_user_prompt}, ] extraction_args = { "model": script_writing_model, "messages": clipper_messages, } if script_writing_model == "o3-mini": extraction_args["reasoning_effort"] = "low" else: extraction_args["max_tokens"] = 5000 extraction_args["temperature"] = 0.45 clipper_response = script_client.chat.completions.create(**extraction_args) start_time = time.time() extraction_response = clipper_response.choices[0].message.content.strip() end_time = time.time() elapsed_time = end_time - start_time script_time_placeholder.markdown(f"⏱️ **Response Time for Script Writing:** {elapsed_time:.2f} seconds") extracted_clip = extraction_response # Save the extraction result for this clip in session state st.session_state[f"extracted_clip_{i}"] = extracted_clip except Exception as e: st.error(f"❌ Extraction error: {str(e)}") # Display extraction output if available if f"extracted_clip_{i}" in st.session_state: st.markdown("#### πŸ“Script: ") st.write(st.session_state[f"extracted_clip_{i}"])