Update app.py

app.py

@@ -7,15 +7,29 @@ import matplotlib.pyplot as plt
 from fpdf import FPDF
 from io import BytesIO
 
-# Set Hugging Face API token from secrets
+# 💬 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"]
 
-# Choose a compatible model (text-generation)
+# 🔗 LangChain setup
 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:
 Product 1:
@@ -28,29 +42,32 @@ Instructions:
 3. Summarize key differentiators between them.
 """
 
-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.")
+# 🎯 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...")
 
-product1 = st.text_area("Enter Product/Service 1 Description", height=200)
-product2 = st.text_area("Enter Product/Service 2 Description", height=200)
+st.markdown("")
 
-if st.button("Compare"):
+# 🚀 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:
-        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)
+
+        # 🧠 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)
 
-        # Basic chart: Number of keywords in each description (just an example)
+        # 📈 Chart Visualization
+        st.markdown("### 🔢 Word Count Breakdown")
         p1_len = len(product1.split())
         p2_len = len(product2.split())
         fig, ax = plt.subplots()
@@ -59,12 +76,11 @@ if st.button("Compare"):
         ax.set_title("Word Count Comparison")
         st.pyplot(fig)
 
-        # 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)
 
@@ -72,5 +88,6 @@ if st.button("Compare"):
             label="📥 Download PDF Report",
             data=pdf_output,
             file_name="competitive_analysis_report.pdf",
-            mime="application/pdf"
+            mime="application/pdf",
+            use_container_width=True
         )