Update app.py

app.py

@@ -147,7 +147,32 @@ def question_create(json_template):
     answer = ""
     for chunk in completion:
         answer += chunk.choices[0].delta.content or ""
-    return answer
+
+
+    client = Groq()
+    completion = client.chat.completions.create(
+        model="llama-3.1-70b-versatile",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are an experienced writer. You will be given an array of questions. \nSome questions will ask to upload images. Ignore any of these type of questions.\nSome questions ask about different identities or descriptions of the same thing. I want you o merge the questions so as to ask input from them once.\nConvert all questions so that more of a professional but also a bit of a funny tone is maintained. \nRETURN AN ARRAY OF THE QUESTIONS ONLY. DO NOT RETURN ANYTHING ELSE. "
+            },
+            {
+                "role": "user",
+                "content": answer
+            }
+        ],
+        temperature=0.73,
+        max_tokens=5840,
+        top_p=1,
+        stream=True,
+        stop=None,
+    )
+    final=""
+    for chunk in completion:
+        final+=chunk.choices[0].delta.content or ""
+
+    return final
 
 def answer_refill(questions,answers,obs_json_template,bizobj_json_template):
 
@@ -190,7 +215,7 @@ def answer_refill(questions,answers,obs_json_template,bizobj_json_template):
             }
         ],
         temperature=1,
-        max_tokens=4610,
+        max_tokens=7610,
         top_p=1,
         stream=True,
         stop=None,
@@ -218,8 +243,8 @@ def executive_summary(json_template):
                 "content": str(json_template)
             }
         ],
-        temperature=1,
-        max_tokens=4610,
+        temperature=0.73,
+        max_tokens=5610,
         top_p=1,
         stream=True,
         stop=None,
@@ -229,11 +254,26 @@ def executive_summary(json_template):
         final_summ+=chunk.choices[0].delta.content or ""
     return final_summ
 
-# Streamlit application
+
 def main():
     st.title("Qualitas Sales Data Collection Chatbot")
     st.caption("Welcome to the Qualitas Bot. First upload a PDF document which should be customer correspondence, detailing some requirements. Also sometimes the Submit button for the questions is a bit sticky. So You might have to click it twice!")
+
     # Initialize session state variables
+    init_session_state()
+
+    # File uploader for the PDF
+    uploaded_file = st.file_uploader("Upload a PDF document", type="pdf")
+    if uploaded_file is not None and not st.session_state.file_processed:
+        st.write("Processing your document...")
+        process_document(uploaded_file)
+        # Display the first question immediately after processing the document
+        show_question()
+
+    # Simulate chat interaction
+    chat_interaction()
+
+def init_session_state():
     if "file_processed" not in st.session_state:
         st.session_state.file_processed = False
     if "questionnaire_started" not in st.session_state:
@@ -247,72 +287,71 @@ def main():
     if "questionnaire_complete" not in st.session_state:
         st.session_state.questionnaire_complete = False
 
-    # File uploader for the PDF
-    uploaded_file = st.file_uploader("Upload a PDF document", type="pdf")
-    answers=[]
-    final_bizobj_json=[]
-    bizobj=[]
-    obs=[]
-    final_obs_json=[]
-    if uploaded_file is not None and not st.session_state.file_processed:
-        st.write("Processing your document...")
-        
-        # Simulate file processing (replace with actual logic)
-        st.session_state.text = extract_text_from_pdf(uploaded_file)
-        st.session_state.classification_result = classification_LLM(st.session_state.text)
-        json_path='observationsJSON.json'
-        with open(json_path, 'r') as file:
-            obs_json_template = json.load(file)
-        final_obs_json=obsjsoncreate(obs_json_template,st.session_state.classification_result,st.session_state.text)
-        st.session_state.obs=final_obs_json
-        json_path='BizObjJSON.json'
-        with open(json_path, 'r') as file:
-            bizobj_json_template = json.load(file)
-        final_bizobj_json=bizobjjsoncreate(bizobj_json_template,st.session_state.text)
-        st.session_state.bizobj=final_bizobj_json
-        questionobs=question_create(final_obs_json)
-        questionbizobj=question_create(final_bizobj_json)
-        st.session_state.questions = ast.literal_eval(questionbizobj) + ast.literal_eval(questionobs)
-        # st.write(st.session_state.questions)
-        
-        # Mark file as processed
-        st.session_state.file_processed = True
-        st.success("Document processed successfully.")
-
-    def show_question():
-        if st.session_state.current_question_index < len(st.session_state.questions):
-            st.write(st.session_state.questions[st.session_state.current_question_index])
-            user_input = st.text_input("Your answer:", key=f"input_{st.session_state.current_question_index}")
-            submit_button = st.button("Submit", key=f"submit_{st.session_state.current_question_index}")
-            
-            if submit_button:
-                # Store the answer
-                st.session_state.messages.append({"role": "user", "content": user_input})
-                # Move to the next question
-                st.session_state.current_question_index += 1
-                
-                # Check if all questions have been answered
-                if st.session_state.current_question_index >= len(st.session_state.questions):
-                    st.session_state.questionnaire_complete = True
-
-    # Main loop to control the flow of the questionnaire
-    if not st.session_state.questionnaire_complete:
+def process_document(uploaded_file):
+    # Simulate file processing (replace with actual logic)
+    st.session_state.text = extract_text_from_pdf(uploaded_file)
+    st.session_state.classification_result = classification_LLM(st.session_state.text)
+    json_path='observationsJSON.json'
+    with open(json_path, 'r') as file:
+        obs_json_template = json.load(file)
+    final_obs_json = obsjsoncreate(obs_json_template, st.session_state.classification_result, st.session_state.text)
+    st.session_state.obs = final_obs_json
+    json_path='BizObjJSON.json'
+    with open(json_path, 'r') as file:
+        bizobj_json_template = json.load(file)
+    final_bizobj_json = bizobjjsoncreate(bizobj_json_template, st.session_state.text)
+    st.session_state.bizobj = final_bizobj_json
+    questionobs = question_create(final_obs_json)
+    questionbizobj = question_create(final_bizobj_json)
+    st.session_state.questions = ast.literal_eval(questionbizobj) + ast.literal_eval(questionobs)
+    st.write(st.session_state.questions)
+    # Mark file as processed
+    st.session_state.file_processed = True
+    st.success("Document processed successfully.")
+
+def chat_interaction():
+    # Display chat messages from history in the correct order
+    for message in st.session_state.messages[::-1]:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # React to user input
+    if prompt := st.chat_input("What is your question?"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+        # Process the user's input and show the next question
         show_question()
+
+def show_question():
+    if st.session_state.current_question_index < len(st.session_state.questions):
+        # Display the next question
+        with st.chat_message("assistant"):
+            st.markdown(st.session_state.questions[st.session_state.current_question_index])
+
+        # Add the question to the chat history
+        st.session_state.messages.append({"role": "assistant", "content": st.session_state.questions[st.session_state.current_question_index]})
+
+        # Move to the next question
+        st.session_state.current_question_index += 1
+
+        # Check if all questions have been answered
+        if st.session_state.current_question_index >= len(st.session_state.questions):
+            st.session_state.questionnaire_complete = True
+            
     else:
         # Display the answers after completing the questionnaire
         answers = [message["content"] for message in st.session_state.messages if message["role"] == "user"]
-        # st.write(st.session_state.questions)
-        # st.write(answers)
-        # st.subheader("Answers")
-        # st.write(answers)
-    if st.session_state.questionnaire_complete:
-        completed_json=answer_refill(st.session_state.questions,answers,st.session_state.obs,st.session_state.bizobj)
-        # st.write(completed_json)
-        exec_summ=executive_summary(completed_json)
+        # with st.chat_message("assistant"):
+            # st.subheader("Answers")
+            # st.write(answers)
+        
+        completed_json = answer_refill(st.session_state.questions, answers, st.session_state.obs, st.session_state.bizobj)
+        exec_summ = executive_summary(completed_json)
         st.write(exec_summ)
-    
 
-    
-                
 if __name__ == "__main__":
-    main()
+    main()