Update app.py

app.py

@@ -1,12 +1,5 @@
-import streamlit as st
-import requests
-import os
-
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.3-70B-Instruct"
-headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
-
 def query_model(product1, product2):
-    prompt = f\"\"\"Compare the following two products or services:
+    prompt = f"""Compare the following two products or services:
 
 Product/Service 1:
 {product1}
@@ -18,23 +11,17 @@ Your task:
 - Provide a detailed feature-by-feature comparison.
 - Perform a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) for each.
 - Highlight key differentiators and insights in a business-focused summary.
-- Use markdown formatting (e.g., bold headings, bullet points) for readability.\"\"\"
+- Use markdown formatting (e.g., bold headings, bullet points) for readability.
+"""
 
     response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
-    result = response.json()
-    return result[0]['generated_text'] if isinstance(result, list) else str(result)
-
-st.set_page_config(page_title="LLM Product Comparison", layout="wide")
-st.title("📊 Product Comparison with Hugging Face LLM")
 
-product1 = st.text_input("Product/Service 1", value="iPhone 13")
-product2 = st.text_input("Product/Service 2", value="iPhone 14")
+    # 🐞 DEBUG: print API response info on screen
+    st.write("🧪 API Status Code:", response.status_code)
+    st.write("🧾 Raw Response:", response.text)
 
-if st.button("Compare"):
-    if not product1 or not product2:
-        st.warning("Please enter both products.")
-    else:
-        with st.spinner("Generating comparison..."):
-            response = query_model(product1, product2)
-        st.markdown("### 📝 Comparison Result")
-        st.markdown(response)
+    try:
+        result = response.json()
+        return result[0]['generated_text'] if isinstance(result, list) else str(result)
+    except Exception as e:
+        return f"⚠️ Error parsing response: {e}"