Delete app.py

app.py

@@ -1,93 +0,0 @@
-import streamlit as st
-from langchain_community.llms import HuggingFaceHub
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
-import os
-import matplotlib.pyplot as plt
-from fpdf import FPDF
-from io import BytesIO
-
-# 💬 Title & Description
-st.set_page_config(page_title="Competitive Analysis Tool", layout="wide")
-st.title("🔍 Competitive Analysis Tool")
-st.markdown("""
-Welcome to your AI-powered business analyst.  
-Compare two products or services to receive:
-- 🧩 Feature-by-feature comparison  
-- 💡 SWOT analysis  
-- 📝 Key differentiators  
-- 📥 Downloadable PDF Report  
-""")
-
-st.markdown("---")
-
-# 🔒 Set API token
-os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
-
-# 🔗 LangChain setup
-llm = HuggingFaceHub(
-    repo_id="tiiuae/falcon-7b-instruct", 
-    model_kwargs={"temperature": 0.7, "max_new_tokens": 1024}
-)
-
-template = """
-Compare the following two products or services:
-Product 1:
-{product1}
-Product 2:
-{product2}
-Instructions:
-1. Provide a feature-by-feature comparison.
-2. Generate a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis for each.
-3. Summarize key differentiators between them.
-"""
-
-prompt = PromptTemplate(input_variables=["product1", "product2"], template=template)
-comparison_chain = LLMChain(llm=llm, prompt=prompt)
-
-# 🎯 UI Layout
-col1, col2 = st.columns(2)
-with col1:
-    product1 = st.text_area("🧩 Product/Service 1", height=200, placeholder="Describe Product 1...")
-with col2:
-    product2 = st.text_area("🧩 Product/Service 2", height=200, placeholder="Describe Product 2...")
-
-st.markdown("")
-
-# 🚀 Compare Button
-if st.button("⚔️ Compare Now", use_container_width=True):
-    if not product1 or not product2:
-        st.warning("Please enter descriptions for both products.")
-    else:
-        with st.spinner("🤖 Analyzing with LLM..."):
-            result = comparison_chain.run(product1=product1, product2=product2)
-
-        # 🧠 Display Results
-        st.subheader("📊 AI-Generated Competitive Analysis")
-        st.markdown(f"""<div style='background-color:#f9f9f9;padding:20px;border-radius:10px;'>{result}</div>""", unsafe_allow_html=True)
-
-        # 📈 Chart Visualization
-        st.markdown("### 🔢 Word Count Breakdown")
-        p1_len = len(product1.split())
-        p2_len = len(product2.split())
-        fig, ax = plt.subplots()
-        ax.bar(["Product 1", "Product 2"], [p1_len, p2_len], color=["skyblue", "salmon"])
-        ax.set_ylabel("Word Count")
-        ax.set_title("Word Count Comparison")
-        st.pyplot(fig)
-
-        # 📥 PDF Report
-        pdf = FPDF()
-        pdf.add_page()
-        pdf.set_font("Arial", size=12)
-        pdf.multi_cell(0, 10, txt="Competitive Analysis Report\n\n" + result)
-        pdf_bytes = pdf.output(dest='S').encode('latin1')
-        pdf_output = BytesIO(pdf_bytes)
-
-        st.download_button(
-            label="📥 Download PDF Report",
-            data=pdf_output,
-            file_name="competitive_analysis_report.pdf",
-            mime="application/pdf",
-            use_container_width=True
-        )