app19

app.py

@@ -24,14 +24,15 @@ from langchain.prompts.prompt import PromptTemplate
 from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
 from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate,  MessagesPlaceholder
 from langchain.document_loaders import PyPDFDirectoryLoader
-
+from pydantic import BaseModel, Field
+from langchain.output_parsers import PydanticOutputParser
 from langchain_community.llms import HuggingFaceHub
 
 from pydantic import BaseModel
 import shutil
 
 # Cell 1: Image Classification Model
-image_pipeline = pipeline(task="image-classification", model="rocioadlc/EfficientNetV2L_TFM")
+image_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")
 
 def predict_image(input_img):
     predictions = image_pipeline(input_img)
@@ -69,26 +70,32 @@ vectordb = Chroma.from_documents(
 )
 # define retriever
 retriever = vectordb.as_retriever(search_kwargs={"k": 1}, search_type="mmr")
-prompt_template = """
-Your name is AngryGreta and you are a recycling chatbot with the objective to anwer questions from user in English or Spanish /
-Use the following pieces of context to answer the question if the question is related with recycling /
-Answer in the same language of the question /
-Always say "thanks for asking!" at the end of the answer /
-If the context is not relevant, please answer the question by using your own knowledge about the topic.
-        
-context: {context}
 
-question: {question}
+class FinalAnswer(BaseModel):
+    question: str = Field(description="the original question")
+    answer: str = Field(description="the extracted answer")
+
+# Assuming you have a parser for the FinalAnswer class
+parser = PydanticOutputParser(pydantic_object=FinalAnswer)
+
+template = """
+Your name is AngryGreta and you are a recycling chatbot with the objective to anwer querys from user in English or Spanish /
+Use the following pieces of context to answer the question /
+Answer in the same language of the question /
+Context: {context}
+Chat history: {chat_history}
+User: {query}
+{format_instructions}
 """
 
 # Create the chat prompt templates
-system_prompt = SystemMessagePromptTemplate.from_template(prompt_template)
+sys_prompt = SystemMessagePromptTemplate.from_template(template)
 qa_prompt = ChatPromptTemplate(
-	messages=[
-		system_prompt,
-        MessagesPlaceholder(variable_name="chat_history"), 
-		HumanMessagePromptTemplate.from_template("{question}")
-    ]
+    messages=[
+        sys_prompt,
+        MessagesPlaceholder(variable_name="chat_history"),
+        HumanMessagePromptTemplate.from_template("{question}")],
+    partial_variables={"format_instructions": parser.get_format_instructions()}
 )
 llm = HuggingFaceHub(
     repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
@@ -101,23 +108,37 @@ llm = HuggingFaceHub(
     },
 )
 
-memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='question', output_key='answer', return_messages=True)
+memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='query', output_key='output', return_messages=True)
 
 qa_chain = ConversationalRetrievalChain.from_llm(
     llm = llm,
+    condense_question_prompt = qa_prompt,
     memory = memory,
     retriever = retriever,
     verbose = True,
     combine_docs_chain_kwargs={'prompt': qa_prompt},
     get_chat_history = lambda h : h,
-    rephrase_question = False,
-    output_key = 'answer'
+    rephrase_question = True,
+    output_key = 'output',
 )
 
-def chat_interface(question,history):
+def chat_interface(question):
+    result = qa_chain.invoke({'query': question, 'context': retriever})
+    output_string = result['output']
+
+    # Find the index of the last occurrence of "answer": in the string
+    answer_index = output_string.rfind('"answer":')
+
+    # Extract the substring starting from the "answer": index
+    answer_part = output_string[answer_index + len('"answer":'):].strip()
+
+    # Find the next occurrence of a double quote to get the start of the answer value
+    quote_index = answer_part.find('"')
 
-    result = qa_chain.invoke({"question": question})
-    return result['answer']
+    # Extract the answer value between double quotes
+    answer_value = answer_part[quote_index + 1:answer_part.find('"', quote_index + 1)]
+    
+    return answer_value
 
 chatbot_gradio_app = gr.ChatInterface(
     fn=chat_interface,