gradio change

app.py

@@ -4,10 +4,10 @@ from langchain.document_loaders import OnlinePDFLoader
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.chat_models import ChatAnthropic
 from langchain.prompts import ChatPromptTemplate
-from langchain.document_loaders import TextLoader
+from transformers import pipeline
 
-# Set API keys from environment variables
-os.environ['WORKING_ANTHROPIC_API_KEY'] = os.getenv("ANTHROPIC_API_KEY")
+# Fetch API key from environment variables
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
 
 pdf_content = ""
 
@@ -20,8 +20,6 @@ def load_pdf(pdf_doc):
         # Load PDF content
         loader = OnlinePDFLoader(pdf_doc.name)
         documents = loader.load()
-
-        # Assuming the `documents` is a list of strings representing each page
         pdf_content = ' '.join(documents)
 
         return "PDF Loaded Successfully."
@@ -30,71 +28,42 @@ def load_pdf(pdf_doc):
         return f"Error processing PDF: {e}"
 
 def chat_with_pdf(question):
-    # Create an instance of the ChatAnthropic model
     model = ChatAnthropic()
-
-    # Define the chat prompt template
     prompt = ChatPromptTemplate.from_messages([
         ("human", pdf_content),
         ("human", question),
-        ("human", "Give a clear summary of this pdf information at a 8th grade reading level.")
+        ("human", "Give a clear summary of this pdf information at an 8th grade reading level.")
     ])
-
-    # Invoke the model using the chain
     chain = prompt | model
     response = chain.invoke({})
-
-    # Get the summary of the PDF content
     summarizer = pipeline("summarization")
     summary = summarizer(pdf_content, max_length=1000, min_length=30, do_sample=False)[0]['summary_text']
-
-    # Combine the chat response and the summary
     combined_response = f"Summary: {summary}\n\nChat Response: {response.content}"
-
     return combined_response
 
-# Define Gradio UI
-def gradio_interface(pdf_doc, question):
-    # ...
-    return gr.Interface(
-        fn=chat_with_pdf,
-        inputs=[pdf_doc, question],
-        outputs=gr.outputs.Textbox(),
-        api_name='chat_with_pdf_2'
-    )
-
 def gradio_interface(pdf_doc, question):
     if not pdf_content:
         return load_pdf(pdf_doc)
     else:
-        # Get the summary of the PDF content
         summarizer = pipeline("summarization")
         summary = summarizer(pdf_content, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
-
-        # Get the chat response
         response = chat_with_pdf(question)
-
-        # Define the outputs
-        summary_output = gr.outputs.Textbox(label="Summary")
-        chat_output = gr.outputs.Textbox(label="Chat Response")
-
-        # Return the Gradio interface with the Multi output
-        return gr.Interface(
-            fn=chat_with_pdf,
-            inputs=[pdf_doc, question],
-            outputs=gradio.outputs.Multi(summary_output, chat_output),
-            examples=[["sample.pdf", "What is this document about?"]],
-            api_name='chat_with_pdf_2'
-        )
-
-gradio_interface(None, None)
-
-
-gr.Interface(fn=gradio_interface,
-             inputs=[gr.File(label="Load a pdf", file_types=['.pdf'], type="file"),
-                     gr.Textbox(label="Ask a question about the PDF")],
-             outputs="text",
-             live=True,
-             title="Chat with PDF content using Anthropic",
-             description="Upload a .PDF and interactively chat about its content."
-            ).launch()
+        return {
+            "Summary": summary,
+            "Chat Response": response
+        }
+
+gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.File(label="Load a pdf", file_types=['.pdf'], type="file"),
+        gr.Textbox(label="Ask a question about the PDF")
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Summary"),
+        gr.outputs.Textbox(label="Chat Response")
+    ],
+    live=True,
+    title="Chat with PDF content using Anthropic",
+    description="Upload a .PDF and interactively chat about its content."
+).launch()