Update app.py

app.py

@@ -1,39 +1,76 @@
 import streamlit as st
-import requests
+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
 
-st.set_page_config(page_title="Competitive Product Analyser", layout="wide")
-st.title("🔍 Competitive Product Analyser")
-st.markdown("Compare two products or services using LLM-powered feature and SWOT analysis.")
+# Set Hugging Face API token from secrets
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]
 
-product1 = st.text_area("🧩 Product/Service 1 Description", height=150)
-product2 = st.text_area("🧩 Product/Service 2 Description", height=150)
+# Choose a compatible model (text-generation)
+llm = HuggingFaceHub(
+    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:
+{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)
+
+# 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)
 
 if st.button("Compare"):
     if not product1 or not product2:
-        st.warning("Please enter both product descriptions.")
+        st.warning("Please enter descriptions for both products.")
     else:
-        with st.spinner("Generating analysis..."):
-            prompt = f"""Compare the following two products/services:
-            
-Product 1: {product1}
-
-Product 2: {product2}
-
-Provide:
-1. A feature-by-feature comparison.
-2. A SWOT analysis for each product/service.
-3. A comparative summary highlighting key differentiators."""
-
-            api_url = "https://api-inference.huggingface.co/models/google/flan-t5-large"
-            headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
-
-            response = requests.post(api_url, headers=headers, json={"inputs": prompt})
-
-            if response.status_code == 200:
-                output = response.json()
-                result = output[0]["generated_text"] if isinstance(output, list) else output.get("generated_text", "")
-                st.subheader("🧠 Analysis")
-                st.write(result)
-            else:
-                st.error(f"API Error: {response.status_code} — {response.text}")
+        result = comparison_chain.run(product1=product1, product2=product2)
+        st.subheader("📊 Comparison Results")
+        st.write(result)
+
+        # 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)
+
+        # Create a downloadable 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",
+            data=pdf_output,
+            file_name="competitive_analysis_report.pdf",
+            mime="application/pdf"
+        )