claude

app.py

@@ -1,15 +1,12 @@
 import gradio as gr
-import os
 from langchain.document_loaders import OnlinePDFLoader
 from langchain.text_splitter import CharacterTextSplitter
-from anthropic import LanguageModel
+from langchain.chat_models import ChatAnthropic
+from langchain.prompts import ChatPromptTemplate
 
 # Set API keys from environment variables
 os.environ['ANTHROPIC_API_KEY'] = os.getenv("Your_Anthropic_API_Key")
 
-# Initialize the Anthropic model
-anthropic_model = LanguageModel(api_key=os.environ['ANTHROPIC_API_KEY'], model="some_model")
-
 pdf_content = ""
 
 def load_pdf(pdf_doc):
@@ -21,7 +18,7 @@ def load_pdf(pdf_doc):
         # Load and split PDF content
         loader = OnlinePDFLoader(pdf_doc.name)
         documents = loader.load()
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
         pdf_content = ' '.join(text_splitter.split_documents(documents))
 
         return "PDF Loaded Successfully."
@@ -30,9 +27,20 @@ def load_pdf(pdf_doc):
         return f"Error processing PDF: {e}"
 
 def chat_with_pdf(question):
-    context = [{"role": "system", "content": pdf_content}]
-    response = anthropic_model.query(question, context=context)
-    return response['answer']
+    # Create an instance of the ChatAnthropic model
+    model = ChatAnthropic()
+
+    # Define the chat prompt template
+    prompt = ChatPromptTemplate.from_messages([
+        ("human", pdf_content),
+        ("human", question),
+    ])
+
+    # Invoke the model using the chain
+    chain = prompt | model
+    response = chain.invoke({})
+    
+    return response.content
 
 # Define Gradio UI
 def gradio_interface(pdf_doc, question):