Update app.py

app.py

@@ -27,7 +27,7 @@ embeddings = HuggingFaceEmbeddings()
 db = FAISS.load_local('db_full', embeddings)
 
 mp_docs = {}
-llm = ClaudeLLM()
+# llm = ClaudeLLM()
 # ChatOpenAI(
 #             temperature=0,
 #             model='gpt-3.5-turbo-16k'
@@ -41,7 +41,7 @@ def add_text(history, text):
 
     return history, ""
 
-pipeline = {'claude': (ClaudeLLM(), 0), 'gpt-3.5': (ChatOpenAI(temperature=0,model='gpt-3.5-turbo-16k'), 65), 'gpt-4': (ChatOpenAI(temperature=0, model='gpt-4'), 30)}
+# pipeline = {'claude': (ClaudeLLM(), 0), 'gpt-3.5': (ChatOpenAI(temperature=0,model='gpt-3.5-turbo-16k'), 65), 'gpt-4': (ChatOpenAI(temperature=0, model='gpt-4'), 30)}
 
 def retrieve_thoughts(query, n):
 
@@ -95,84 +95,14 @@ def qa_retrieve(query, llm):
 
     tier_1 = thoughts['tier 1']
     tier_2 = thoughts['tier 2']
-
-    reference = tier_1[['ref', 'url', 'title']].to_dict('records')
-
-    tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n Content: {x.content}", axis = 1).values)
-    print(len(tier_1))
-    tier_2 = list(tier_2.apply(lambda x: f"title: {x['title']}\n Content: {x.content}", axis = 1).values)
-
-    print(f"QUERY: {query}\nTIER 1: {tier_1}\nTIER2: {tier_2}")
-    # print(f"DOCS RETRIEVED: {mp_docs.values}")
-
-    # Cynthesis Generation
-    session_prompt = """ A bot that is open to discussions about different cultural, philosophical and political exchanges. You will use do different analysis to the articles provided to me. Stay truthful and if you weren't provided any resources give your oppinion only."""
-    task = """Your primary responsibility is to identify multiple themes from the given articles. For each theme detected, you are to present it under three separate categories:
-    
-    1. Theme Title - An easy-to-understand title that encapsulates the core idea of the theme extracted from the article.
-    
-    2. Theme Description - An expanded elaboration that explores the theme in detail based on the arguments and points provided in the article.
-    
-    3. Quotes related to theme - Locate and provide at least one compelling quote from the article that directly supports or showcases the theme you have identified. This quote should serve as a specific evidence or example from the article text that corresponds directly to the developed theme.
     
-    The extracted themes should be written in structured manner, ensuring clarity and meaningful correlation between the themes and the articles. Make sure your analysis is rooted in the arguments given in the article. Avoid including personal opinions or making generalizations that are not explicitly supported by the articles. """
-
-
-    prompt = PromptTemplate(
-            input_variables=["query", "task", "session_prompt", "articles"],
-            template="""
-            You are a {session_prompt}
-            {task}
-
-            query: {query}
-
-            Articles:
-            {articles}
-
-            
-            The extracted themes should be written in structured manner, ensuring clarity and meaningful correlation between the themes and the articles. Make sure your analysis is rooted in the arguments given in the article. Avoid including personal opinions or making generalizations that are not explicitly supported by the articles.
-
-            """,
-        )
-
-
-    # llm = BardLLM()
-    chain = LLMChain(llm=llm, prompt = prompt)
-
-    response = chain.run(query=query, articles="\n".join(tier_1), session_prompt = session_prompt, task = task)
-
-    for i in range(5):
-      response = response.replace(f'[{i}]', f"<span class='text-primary'>[{i}]</span>")
-
-    # Generate related questions
-    prompt_q = PromptTemplate(
-            input_variables=[ "session_prompt", "articles"],
-            template="""
-            You are a {session_prompt}
-            Give general/global questions related the following articles:
-
-            Articles:
-            {articles}
-
-
-            Make sure not to ask specific questions, keep them general, short and concise.
-            """,
-        )
-
-    chain_q = LLMChain(llm=ClaudeLLM(), prompt = prompt_q)
-
-    questions = chain_q.run(session_prompt = session_prompt, articles = "\n".join(tier_2), )
-    print(questions)
-    questions = questions[questions.index('1'):]
-
-    questions = [ remove_numbers(t).strip() for (i, t) in enumerate(questions.split('.'))  if len(t) > 5][:5]
-    print(questions)
-
-    # TO-DO: initiate models in another function, refactor code to be reusable
+    reference = tier_1[['ref', 'url', 'title', 'content','score']].to_dict('records')
 
-    # json_resp = {'cynthesis': response, 'questions': questions, 'Reference': reference}
+    # tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n Content: {x.content}", axis = 1).values)
+    # print(len(tier_1))
+    # tier_2 = list(tier_2.apply(lambda x: f"title: {x['title']}\n Content: {x.content}", axis = 1).values)
 
-    return response, {'Reference': reference}
+    return {'Reference': reference}
 
 def flush():
   return None
@@ -184,7 +114,7 @@ examples = [
 
 demo = gr.Interface(fn=qa_retrieve, title="cicero-qa-api",
                      inputs=gr.inputs.Textbox(lines=5, label="what would you like to learn about?"),
-                     outputs=[gr.components.Textbox(lines=3, label="Themes"),
+                     outputs=[
                              gr.components.JSON( label="Reference")],examples=examples)
 
 demo.queue(concurrency_count = 4)