Update app.py

app.py

@@ -106,10 +106,10 @@ def prompt_engineer(text, longtext, query):
     # summary = summarizer(prompt, max_length=1024, min_length=50)[0]["summary_text"]
 
     try:
-        llm = HuggingFaceHub(
-            repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0, "max_new_tokens": 256, "task":"text-generation"}
+        sllm = HuggingFaceHub(
+            repo_id="facebook/bart-large-cnn", model_kwargs={"temperature": 0, "max_new_tokens": 256, "task":"text-generation"}
         )
-        st.write("llm connection started..")
+        st.write("summary llm connection started..")
     except Exception as e:
         st.error(f"Error invoke: {e}")
 
@@ -126,7 +126,7 @@ def prompt_engineer(text, longtext, query):
     )
     
     # Instantiate chain
-    chain = create_stuff_documents_chain(llm, prompt)
+    chain = create_stuff_documents_chain(sllm, prompt)
     
     # Invoke chain
     summary = chain.invoke({"context": [docs]})
@@ -158,9 +158,10 @@ def prompt_engineer(text, longtext, query):
     result = ""
     
     try:
-        # llm = HuggingFaceHub(
-        #     repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0, "max_new_tokens": 256, "task":"text-generation"}
-        # )
+        llm = HuggingFaceHub(
+            repo_id="meta-llama/Meta-Llama-3-8B-Instruct", model_kwargs={"temperature": 0, "max_new_tokens": 256, "task":"text-generation"}
+        )
+        st.write("summary llm connection started..")
         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]