Update app.py

app.py

@@ -88,7 +88,7 @@ def get_pinecone_semantic_index(pinecone):
 
 
 def prompt_engineer(text, longtext, query):
-    summary_prompt_template = """
+    summary_prompt_inst = """
     write a concise summary of the following text delimited by triple backquotes.
     return your response in bullet points which convers the key points of the text.
 
@@ -105,37 +105,39 @@ def prompt_engineer(text, longtext, query):
     # Generate the summary
     # summary = summarizer(prompt, max_length=1024, min_length=50)[0]["summary_text"]
 
-    try:
-        sllm = HuggingFaceHub(
-            repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0.1, "max_new_tokens": 256, "task":"summarization"}
-        )
-        st.write("Summary Chat llm connection started..")
-    except Exception as e:
-        st.error(f"Error invoke: {e}")
-
-    from langchain.chains.combine_documents import create_stuff_documents_chain
-    from langchain.chains.llm import LLMChain
-    from langchain_core.prompts import ChatPromptTemplate
-    from langchain_core.documents import Document
-
-    docs =  Document(page_content=longtext, metadata={"source": "pinecone"})
-    st.write(docs)
-    # Define prompt
-    prompt = ChatPromptTemplate.from_messages(
-        [("system", summary_prompt_template)]
-    )
+    # try:
+    #     sllm = HuggingFaceHub(
+    #         repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0.1, "max_new_tokens": 256, "task":"summarization"}
+    #     )
+    #     st.write("Summary Chat llm connection started..")
+    # except Exception as e:
+    #     st.error(f"Error invoke: {e}")
+
+    # from langchain.chains.combine_documents import create_stuff_documents_chain
+    # from langchain.chains.llm import LLMChain
+    # from langchain_core.prompts import ChatPromptTemplate
+    # from langchain_core.documents import Document
+
+    # docs =  Document(page_content=longtext, metadata={"source": "pinecone"})
+    # st.write(docs)
+    # # Define prompt
+    # prompt = ChatPromptTemplate.from_messages(
+    #     [("system", summary_prompt_template)]
+    # )
     
-    # Instantiate chain
-    chain = create_stuff_documents_chain(sllm, prompt)
+    # # Instantiate chain
+    # chain = create_stuff_documents_chain(sllm, prompt)
     
-    # Invoke chain
-    summary = chain.invoke({"context": [docs]})
+    # # Invoke chain
+    # summary = chain.invoke({"context": [docs]})
+
+    summary_prompt_template = ChatPromptTemplate.from_template(summary_prompt_inst)
+    summary_prompt = summary_prompt_template.format(context=longtext, question="generate summary of text?")
     
     with st.sidebar:
         st.divider()
         st.markdown("*:red[Text Summary Generation]* from above Top 5 **:green[similarity search results]**.")
-        st.write(summary)
-        st.divider()
+
 
     GENERATION_PROMPT_TEMPLATE = """
     Instructions:
@@ -162,6 +164,9 @@ def prompt_engineer(text, longtext, query):
             repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0, "task":"text-generation"}
         )
         st.write("GEN llm connection started..")
+        summary = llm.invoke(summary_prompt)
+        st.write(summary)
+        st.divider()
         response_text = llm.invoke(prompt)
         escaped_query = re.escape(query)
         result = re.split(f'Answer the question based on the above context: {escaped_query}\n',response_text)[-1]