fixed redundancy with q&a

app.py

@@ -105,6 +105,7 @@ def generate_text_chunks_lib():
     key_engine = Keywords(title_entry)
     keywords = key_engine.get_keywords(text_chunks_lib)
 
+
 # =========== SIDEBAR FOR GENERATION ===========
 with st.sidebar:
     youtube_link = st.text_input(label = "Type in your Youtube link", placeholder = "", key="url")
@@ -258,88 +259,86 @@ with tab5:
 
 # =========== QUERY BOT ===========
 with tab6:
-    if is_completed_analysis:
-        if 'generated' not in st.session_state:
-            st.session_state['generated'] = []
-
-        if 'past' not in st.session_state:
-            st.session_state['past'] = []
-
-        def get_text():
-            st.header("Ask me something about the video:")
-            input_text = st.text_input("You: ", key="prompt")
-            return input_text
-
-
-        def get_embedding_text(prompt):
-            response = openai.Embedding.create(
-                input= prompt.strip(),
-                model="text-embedding-ada-002"
-            )
-            q_embedding = response['data'][0]['embedding']
-            print("the folder name at got here 1.5 is ", folder_name)
-            # df = pd.read_csv(f'{folder_name}/word_embeddings.csv', index_col=0)
-            data['embedding'] = data['embedding'].apply(eval).apply(np.array)
-
-            data['distances'] = distances_from_embeddings(q_embedding, data['embedding'].values, distance_metric='cosine')
-            returns = []
-            
-            # Sort by distance with 2 hints
-            for i, row in data.sort_values('distances', ascending=True).head(4).iterrows():
-                # Else add it to the text that is being returned
-                returns.append(row["text"])
-
-            # Return the context
-            return "\n\n###\n\n".join(returns)
-
-        def generate_response(prompt):
-            one_shot_prompt = '''
-                I am YoutubeGPT, a highly intelligent question answering bot.
-                If you ask me a question that is rooted in truth, I will give you the answer.
-                Q: What is human life expectancy in the United States?
-                A: Human life expectancy in the United States is 78 years.
-                Q: '''+prompt+'''
-                A: 
-            '''
-            completions = openai.Completion.create(
-                engine = "text-davinci-003",
-                prompt = one_shot_prompt,
-                max_tokens = 1024,
-                n = 1,
-                stop=["Q:"],
-                temperature=0.5,
-            )
-            message = completions.choices[0].text
-            return message
-        
-        if is_completed_analysis:
-            user_input = get_text()
-            print("user input is ", user_input)
-            print("the folder name at got here 0.5 is ", folder_name)
-        else:
-            user_input = None
+    
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = []
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = []
+
+    def get_text():
+        st.header("Ask me something about the video:")
+        input_text = st.text_input("You: ", key="prompt")
+        return input_text
+
+
+    def get_embedding_text(prompt):
+        response = openai.Embedding.create(
+            input= prompt.strip(),
+            model="text-embedding-ada-002"
+        )
+        q_embedding = response['data'][0]['embedding']
+        print("the folder name at got here 1.5 is ", folder_name)
+        # df = pd.read_csv(f'{folder_name}/word_embeddings.csv', index_col=0)
+        data['embedding'] = data['embedding'].apply(eval).apply(np.array)
+
+        data['distances'] = distances_from_embeddings(q_embedding, data['embedding'].values, distance_metric='cosine')
+        returns = []
         
-        if 'messages' not in st.session_state:
-            st.session_state['messages'] = get_initial_message()
+        # Sort by distance with 2 hints
+        for i, row in data.sort_values('distances', ascending=True).head(4).iterrows():
+            # Else add it to the text that is being returned
+            returns.append(row["text"])
+
+        # Return the context
+        return "\n\n###\n\n".join(returns)
+
+    def generate_response(prompt):
+        one_shot_prompt = '''
+            I am YoutubeGPT, a highly intelligent question answering bot.
+            If you ask me a question that is rooted in truth, I will give you the answer.
+            Q: What is human life expectancy in the United States?
+            A: Human life expectancy in the United States is 78 years.
+            Q: '''+prompt+'''
+            A: 
+        '''
+        completions = openai.Completion.create(
+            engine = "text-davinci-003",
+            prompt = one_shot_prompt,
+            max_tokens = 1024,
+            n = 1,
+            stop=["Q:"],
+            temperature=0.5,
+        )
+        message = completions.choices[0].text
+        return message
+    
+    
+    user_input = get_text()
+    print("user input is ", user_input)
+    print("the folder name at got here 0.5 is ", folder_name)    
+    
+    # if 'messages' not in st.session_state:
+    #     st.session_state['messages'] = get_initial_message()
+    
+    if user_input:
+        print("got here 1")
+        print("the folder name at got here 1.5 is ", folder_name)
+        text_embedding = get_embedding_text(user_input)
+        print("the folder name at got here 1.5 is ", folder_name)
+        print("got here 2")
+        title = data_transcription['title']
+        string_title = "\n\n###\n\n".join(title)
+        user_input_embedding = 'Using this context: "'+string_title+'. '+text_embedding+'", answer the following question. \n'+user_input
+        print("got here 3")
+        output = generate_response(user_input_embedding)
+        st.session_state.past.append(user_input)
+        st.session_state.generated.append(output)
         
-        if user_input:
-            print("got here 1")
-            print("the folder name at got here 1.5 is ", folder_name)
-            text_embedding = get_embedding_text(user_input)
-            print("the folder name at got here 1.5 is ", folder_name)
-            print("got here 2")
-            title = data_transcription['title']
-            string_title = "\n\n###\n\n".join(title)
-            user_input_embedding = 'Using this context: "'+string_title+'. '+text_embedding+'", answer the following question. \n'+user_input
-            print("got here 3")
-            output = generate_response(user_input_embedding)
-            st.session_state.past.append(user_input)
-            st.session_state.generated.append(output)
-            
-        if st.session_state['generated']:
-            for i in range(len(st.session_state['generated'])-1, -1, -1):
-                message(st.session_state["generated"][i], key=str(i))
-                message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
+    if st.session_state['generated']:
+        for i in range(len(st.session_state['generated'])-1, -1, -1):
+            message(st.session_state["generated"][i], key=str(i))
+            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
 
 
 # st.header("What else")