| import streamlit as st |
| from langchain_community.llms import HuggingFaceHub |
| import os |
| from fpdf import FPDF |
| from io import BytesIO |
| import textwrap |
| import re |
|
|
| |
| st.set_page_config(page_title="Pro Competitive Analysis", layout="centered") |
| st.markdown(""" |
| <style> |
| body { |
| background-color: #111 !important; |
| color: #e0e0e0; |
| font-family: 'Segoe UI', sans-serif; |
| } |
| h1, h2, h3 { |
| color: #ffffff; |
| } |
| textarea, input, button, .stTextInput>div>div>input { |
| background-color: #1c1c1c !important; |
| color: #f1f1f1 !important; |
| border: 1px solid #444 !important; |
| } |
| button:hover { |
| background-color: #444 !important; |
| } |
| .stDownloadButton>button { |
| background-color: #222 !important; |
| color: white; |
| } |
| .stDownloadButton>button:hover { |
| background-color: #444 !important; |
| } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| |
| st.title("💼 Competitive Analysis Pro") |
| st.markdown("Get an expert-level markdown analysis between two products or services, including SWOT, features, and recommendations.") |
|
|
| |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"] |
|
|
| |
| llm = HuggingFaceHub( |
| repo_id="HuggingFaceH4/zephyr-7b-alpha", |
| model_kwargs={"temperature": 0.7, "max_new_tokens": 1024} |
| ) |
|
|
| |
| product1 = st.text_area("🧩 Product/Service 1", height=200, placeholder="e.g., iPhone 13") |
| product2 = st.text_area("🧩 Product/Service 2", height=200, placeholder="e.g., iPhone 14") |
|
|
| |
| if st.button("🔍 Run Competitive Analysis", use_container_width=True): |
| if not product1 or not product2: |
| st.warning("Please enter both product descriptions.") |
| else: |
| with st.spinner("🧠 Generating insights with Zephyr..."): |
| prompt = f""" |
| Compare the following two products or services: |
| Product 1: |
| {product1} |
| Product 2: |
| {product2} |
| Return a professional analysis in markdown format that includes: |
| - Feature-by-feature comparison |
| - SWOT analysis for each product |
| - Business use cases and recommendations |
| - Key differentiators and which product suits which audience |
| Do NOT include or mention these instructions. |
| Only return the clean markdown report. |
| """ |
| result = llm(prompt) |
|
|
| |
| st.markdown("### 📊 Expert Comparison") |
| st.markdown(result) |
