Added chatbot code

app.py

@@ -8,6 +8,8 @@ from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.chains import RetrievalQA
+from streamlit_chat import message
+
 
 @st.cache_resource
 def get_model():
@@ -32,7 +34,7 @@ def llm_pipeline():
         'text2text-generation',
         model = base_model,
         tokenizer=tokenizer,
-        max_length = 256,
+        max_length = 512,
         do_sample = True,
         temperature = 0.3,
         top_p = 0.95,
@@ -65,21 +67,63 @@ def process_answer(instruction):
     answer = generated_text['result']
     return answer, generated_text
 
+# Display conversation history using Streamlit messages
+def display_conversation(history):
+    st.write(history)
+    for i in range(len(history["generated"])):
+        message(history["past"][i] , is_user=True, key= str(i) + "_user")
+        if isinstance(history["generated"][i],str):
+          message(history["generated"][i] , key= str(i))
+        else:
+          message(history["generated"][i][0] , key= str(i))
+          for source in history["generated"][i][1]['source_documents']:
+            st.write(source.metadata['source'])
+
 def main():
-    st.title("Search your pdf📚")
+    # Search with pdf code
+    # st.title("Search your pdf📚")
+    # with st.expander("About the App"):
+    #     st.markdown(
+    # """This is a Generative AI powered Question and Answering app that responds to questions about your PDF file.
+    # """
+    #     )
+
+    # question = st.text_area("Enter Your Question")
+    # if st.button("Search"):
+    #     st.info("Your question: "+question)
+    #     st.info("Your Answer")
+    #     answer, metadata = process_answer(question)
+    #     st.write(answer)
+    #     st.write(metadata)
+
+    # Chat with pdf code
+    st.title("Chat with your pdf📚")
     with st.expander("About the App"):
         st.markdown(
-    """This is a Generative AI powered Question and Answering app that responds to questions about your PDF file.
-    """
+        """
+        This is a Generative AI powered Question and Answering app that responds to questions about your PDF file.
+        """
         )
 
-    question = st.text_area("Enter Your Question")
-    if st.button("Search"):
-        st.info("Your question: "+question)
-        st.info("Your Answer")
-        answer, metadata = process_answer(question)
-        st.write(answer)
-        st.write(metadata)
+    user_input = st.text_input("",key="input")
+
+    # Initialize session state for generated responses and past messages
+    if "generated" not in st.session_state:
+        st.session_state["generated"] = ["I am ready to help you"]
+    if "past" not in st.session_state:
+        st.session_state["past"] = ["Hey There!"]
+
+    # Search the database for a response based on user input and update session state
+    if user_input:
+        answer = process_answer({"query" : user_input})
+        st.session_state["past"].append(user_input)
+        response = answer
+        st.session_state["generated"].append(response)
+
+    # Display Conversation history using Streamlit messages
+    if st.session_state["generated"]:
+        display_conversation(st.session_state)
+
 
 
 if __name__ == "__main__":