Update app.py

app.py

@@ -56,7 +56,7 @@ if pdf_source == "Upload a PDF file":
 elif pdf_source == "Enter a PDF URL":
     pdf_url = st.text_input("Enter PDF URL:", value="https://arxiv.org/pdf/2406.06998", key="pdf_url")
 
-    # Button to manually trigger the download
+    # ✅ Button to manually trigger the download
     if st.button("Download and Process PDF") or pdf_url.strip() != "" and st.session_state.get("pdf_path") is None:
         with st.spinner("Downloading PDF..."):
             try:
@@ -198,14 +198,10 @@ if query:
         
         QUERY:
         {retriever_query}
-        You MUST respond in valid JSON format only.  
-        DO NOT add any explanations, thoughts, or text outside the JSON. 
-        ### **Example JSON Format**
-        ```json
-        [
-        {"content": 1, "score": 0, "reasoning": "The content is relevant."},
-        {"content": 2, "score": 1, "reasoning": "The content is irrelevant."}
-        ]
+        Provide your verdict in JSON format  with a single key 'score' and no preamble or explanation:
+        [{{"content:1,"score": <your score either 0 or 1>,"Reasoning":<why you have chose the score as 0 or 1>}},
+        {{"content:2,"score": <your score either 0 or 1>,"Reasoning":<why you have chose the score as 0 or 1>}},
+        ...]
         """
         
         context_relevancy_checker_prompt = PromptTemplate(input_variables=["retriever_query","context"],template=relevancy_prompt)
@@ -265,28 +261,6 @@ 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")
 
@@ -312,13 +286,13 @@ if query:
         final_output = context_management_chain({"context":context,"retriever_query":query,"query":query})
 
         st.subheader('final_output["relevancy_response"]')
-        st.write(final_output["relevancy_response"] )
+        st.json(final_output["relevancy_response"] )
 
         st.subheader('final_output["context_number"]')
-        st.write(final_output["context_number"])
+        st.json(final_output["context_number"])
 
         st.subheader('final_output["relevant_contexts"]')
-        st.write(final_output["relevant_contexts"])
+        st.json(final_output["relevant_contexts"])
 
         st.subheader('final_output["final_response"]')
-        st.write(final_output["final_response"])
+        st.json(final_output["final_response"])