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app.py

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+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
+from streamlit_lottie import st_lottie
+import requests
+
+# Load Lottie animation
+def load_lottieurl(url):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+# Page config
+st.set_page_config(page_title="Competitive Product Analyser", layout="wide")
+
+# Hero header
+st.markdown("""
+<div style='background: linear-gradient(90deg, #ff4b4b, #ff6e6e); 
+            padding: 30px; 
+            border-radius: 10px; 
+            text-align: center; 
+            color: white; 
+            font-size: 28px;
+            font-weight: bold'>
+🚀 Competitive Product Analyser
+</div>
+""", unsafe_allow_html=True)
+
+st.markdown("### Compare two products or services and generate a full analysis including feature comparisons, SWOT, and more.")
+st.markdown("---")
+
+# Set token
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
+
+# Model 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)
+
+# Input 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...")
+
+# 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)
+
+        # Output display
+        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)
+
+        # Word count metrics
+        col1, col2 = st.columns(2)
+        col1.metric("📄 Product 1 Words", f"{len(product1.split())}")
+        col2.metric("📄 Product 2 Words", f"{len(product2.split())}")
+
+        # Chart
+        st.markdown("### 🔢 Word Count Comparison")
+        fig, ax = plt.subplots()
+        ax.bar(["Product 1", "Product 2"], [len(product1.split()), len(product2.split())], color=["skyblue", "salmon"])
+        ax.set_ylabel("Word Count")
+        ax.set_title("Word Count Chart")
+        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
+        )
+
+# Footer
+st.markdown("""
+---
+<div style='text-align: center; color: gray; font-size: 14px;'>
+Made with ❤️ using Streamlit, LangChain, and Hugging Face.
+</div>
+""", unsafe_allow_html=True)

requirements.txt

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+streamlit
+requests
+matplotlib
+fpdf
+langchain
+streamlit-lottie