Update app.py

app.py

@@ -3,74 +3,100 @@ 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
 
-# Set Hugging Face API token from secrets
+# Page config
+st.set_page_config(page_title="Pro Competitive Analysis Tool", layout="centered")
+
+# Stylish monochrome header
+st.markdown("""
+<style>
+body {
+    background-color: #111;
+    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("Gain deep market insight with feature breakdowns, SWOT, and key differentiators.")
+
+# Set API key from Hugging Face Secrets
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
 
-# Choose a compatible model (text-generation)
+# Load model
 llm = HuggingFaceHub(
-    repo_id="tiiuae/falcon-7b-instruct", model_kwargs={"temperature": 0.7, "max_new_tokens": 1024}
+    repo_id="tiiuae/falcon-7b-instruct",
+    model_kwargs={"temperature": 0.7, "max_new_tokens": 1024}
 )
 
 # Prompt template
 template = """
-Compare the following two products or services:
+You are a professional market analyst. Analyze the two following products/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.
+1. Provide a detailed feature-by-feature comparison (performance, pricing, usability, support, integrations, etc.).
+2. Conduct a comprehensive SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis for each.
+3. Offer business insights, use cases, and suggestions for each.
+4. Summarize the key differentiators and recommend which is better for different types of users or companies.
+Use professional tone and structured markdown formatting (with bold headings and bullet points).
 """
 
-prompt = PromptTemplate(
-    input_variables=["product1", "product2"],
-    template=template,
-)
-
+prompt = PromptTemplate(input_variables=["product1", "product2"], template=template)
 comparison_chain = LLMChain(llm=llm, prompt=prompt)
 
-# Streamlit UI
-st.title("🔍 Competitive Analysis Tool")
-st.write("Compare two products or services using LLM-powered insights.")
-
-product1 = st.text_area("Enter Product/Service 1 Description", height=200)
-product2 = st.text_area("Enter Product/Service 2 Description", height=200)
+# Input UI
+product1 = st.text_area("🧩 Product/Service 1", height=200, placeholder="e.g., Salesforce CRM")
+product2 = st.text_area("🧩 Product/Service 2", height=200, placeholder="e.g., Zoho CRM")
 
-if st.button("Compare"):
+if st.button("🔍 Run Competitive Analysis", use_container_width=True):
     if not product1 or not product2:
         st.warning("Please enter descriptions for both products.")
     else:
-        result = comparison_chain.run(product1=product1, product2=product2)
-        st.subheader("📊 Comparison Results")
-        st.write(result)
+        with st.spinner("Analyzing with LLM..."):
+            result = comparison_chain.run(product1=product1, product2=product2)
 
-        # Basic chart: Number of keywords in each description (just an example)
-        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)
+        st.markdown("### 📊 Analysis Report")
+        st.markdown(result)
 
-        # Create a downloadable PDF report
+        # 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)
-        # Generate PDF content as string and convert to BytesIO
         pdf_bytes = pdf.output(dest='S').encode('latin1')
         pdf_output = BytesIO(pdf_bytes)
 
         st.download_button(
-            label="📥 Download PDF Report",
+            label="📥 Download Full Report as PDF",
             data=pdf_output,
             file_name="competitive_analysis_report.pdf",
-            mime="application/pdf"
+            mime="application/pdf",
+            use_container_width=True
         )