Update app.py

app.py

@@ -1,12 +1,12 @@
 import streamlit as st
-from langchain_community.llms import HuggingFaceHub
+from langchain_community.chat_models import ChatHuggingFace
 import os
 from fpdf import FPDF
 from io import BytesIO
 import textwrap
 import re
 
-# Set page layout and dark theme styling
+# Page config and custom dark theme
 st.set_page_config(page_title="Pro Competitive Analysis", layout="centered")
 st.markdown("""
 <style>
@@ -36,74 +36,63 @@ button:hover {
 </style>
 """, unsafe_allow_html=True)
 
-# App header
+# Header
 st.title("💼 Competitive Analysis Pro")
-st.markdown("Generate expert-level comparisons between two products or services, with SWOT, insights, and business recommendations.")
+st.markdown("Get a detailed, markdown-formatted expert comparison of two products or services. Including SWOT, features, use cases & more.")
 
-# Set Hugging Face API key (securely via Secrets)
+# Set API token
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
 
-# LLM Setup
-llm = HuggingFaceHub(
-    repo_id="tiiuae/falcon-7b-instruct",
+# Load chat model (LLaMA 2)
+llm = ChatHuggingFace(
+    repo_id="meta-llama/Llama-2-13b-chat-hf",
+    task="chat-completion",
     model_kwargs={"temperature": 0.7, "max_new_tokens": 1024}
 )
 
-# Input Fields
+# User input
 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")
 
-# Button logic
+# Generate button
 if st.button("🔍 Run Competitive Analysis", use_container_width=True):
     if not product1 or not product2:
         st.warning("Please enter both product descriptions.")
     else:
-        # LLM prompt (not printed)
-        system_instruction = """
-You are a professional market analyst trained to evaluate two competing products or services.
-
-Write an in-depth comparative analysis in **markdown format**, covering:
-
-1. Feature-by-Feature Comparison:
-
-- For both Product 1 and Product 2 separately
-
-- Hardware specifications
-- Design and display
-- Performance (CPU, GPU, etc.)
-- Battery life and charging
-- Software experience
-- Ecosystem/integration
-- Customer support
-- Price and value proposition
-
-2. SWOT Analysis:
-- For both Product 1 and Product 2 separately
-
-3. Business Use Cases & Recommendations:
-- Target user base (consumer, enterprise, creators, etc.)
-- Best use cases
-- Strategic positioning
-
-4. Key Differentiators and Summary:
-- Highlight which product is better for which audience
-- Summarize standout features
-
-Dont write these system_instructions while printing 
-"""
-        user_input = f"\n\nProduct 1:\n{product1}\n\nProduct 2:\n{product2}"
-        final_input = system_instruction.strip() + user_input
-
-        with st.spinner("🧠 Generating detailed analysis..."):
-            result = llm(final_input)
-
+        with st.spinner("🧠 Analyzing with LLaMA 2..."):
+
+            # Construct system & user prompt (chat style)
+            chat_input = [
+                {
+                    "role": "system",
+                    "content": "You are a professional market analyst. Only return markdown-formatted analysis — do not include system instructions."
+                },
+                {
+                    "role": "user",
+                    "content": f"""Compare the following two products/services:
+
+Product 1:
+{product1}
+
+Product 2:
+{product2}
+
+Return a detailed 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"""
+                }
+            ]
+
+            result = llm(chat_input)
+
+        # Display result
         st.markdown("### 📊 Expert Comparison")
         st.markdown(result)
 
-        # Clean markdown & sanitize output
+        # Clean & wrap output for PDF
         clean_text = result.replace("**", "").replace("*", "").replace("`", "").replace("#", "").replace("•", "-")
-
-        # PDF Generation (safe against long unbreakable lines)
         pdf = FPDF()
         pdf.add_page()
         pdf.set_font("Arial", size=12)
@@ -123,14 +112,14 @@ Dont write these system_instructions while printing
                     wrapped_lines = safe_line_split(line)
                 for wl in wrapped_lines:
                     pdf.multi_cell(0, 10, wl)
-            except Exception as e:
-                print(f"⚠️ Skipped line due to error: {line[:50]}...")
+            except Exception:
+                continue  # Skip bad lines
 
         pdf_output = BytesIO()
         pdf.output(pdf_output)
         pdf_output.seek(0)
 
-        # Streamlit download button
+        # Download button
         st.download_button(
             label="📥 Download Report as PDF",
             data=pdf_output,