Update app.py

app.py

@@ -259,6 +259,28 @@ if query:
         context_relevancy_evaluation_chain = LLMChain(llm=llm_judge, prompt=context_relevancy_checker_prompt, output_key="relevancy_response")
 
         response_crisis = context_relevancy_evaluation_chain.invoke({"context":context,"retriever_query":query})
+        
+        # Debug Raw LLM Output
+        st.write("🛠️ Debugging: Raw LLM Response for Relevancy:", response_crisis['relevancy_response'])
+        
+        # Extract raw JSON response
+        raw_response = response_crisis['relevancy_response']
+        
+        # 🔧 **Sanitize the response by removing `<think>` or unwanted text**
+        if "<think>" in raw_response:
+            raw_response = raw_response.split("<think>")[-1]  # Keep only JSON part
+        if "</think>" in raw_response:
+            raw_response = raw_response.split("</think>")[0]  # Remove trailing text
+            
+        # 🔍 **Try parsing the JSON safely**
+        try:
+            relevancy_response = json.loads(raw_response)
+            st.write("✅ Successfully parsed JSON:", relevancy_response)  # Debugging output
+        except json.JSONDecodeError as e:
+            st.error(f"❌ Failed to parse JSON: {e}")
+            st.write("🔍 Raw LLM Response Before Parsing:", raw_response)  # Debugging output
+            relevancy_response = None  # Prevent breaking the pipeline
+
 
         pick_relevant_context_chain = LLMChain(llm=llm_judge, prompt=relevant_prompt, output_key="context_number")