Update app.py

app.py

@@ -1,41 +1,55 @@
 import os
-from langchain import PromptTemplate
+from langchain_core.prompts import PromptTemplate
 from langchain.chains.question_answering import load_qa_chain
-from langchain.document_loaders import PyPDFLoader
-from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.document_loaders import PyPDFLoader
 import google.generativeai as genai
-import gradio as gr
+from gradio import Interface
 
 # Function for initialization
 def initialize(pdf_file, question):
-    with open("/tmp/uploaded_file.pdf", "wb") as f:
-        f.write(pdf_file.read())
-    file_path = "/tmp/uploaded_file.pdf"
-    genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-    model = genai.GenerativeModel('gemini-pro')
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
-    prompt_template = """Answer the question as precise as possible using the provided context. If the answer is
-                          not contained in the context, say "answer not available in context" \n\n
-                          Context: \n {context}?\n
-                          Question: \n {question} \n
-                          Answer:
-                        """
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-    if os.path.exists(file_path):
-        pdf_loader = PyPDFLoader(file_path)
-        pages = pdf_loader.load_and_split()
-        context = "\n".join(str(page.page_content) for page in pages[:30])
-        stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-        stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
-        return stuff_answer['output_text']
-    else:
-        return "Error: Unable to process the document. Please ensure the PDF file is valid."
-
-# Create a Gradio interface for file upload and question input
-iface = gr.Interface(
+    try:
+        # Save the uploaded PDF content temporarily
+        with open("/tmp/uploaded_file.pdf", "wb") as f:
+            f.write(pdf_file.read())
+        file_path = "/tmp/uploaded_file.pdf"
+
+        # Configure Google Generative AI
+        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+        model = genai.GenerativeModel('gemini-pro')
+        model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+
+        # Prompt template for formatting context and question
+        prompt_template = """Answer the question as precise as possible using the provided context. If the answer is not contained in the context, say "answer not available in context" 
+
+Context: 
+{context}
+
+Question: 
+{question} 
+
+Answer:
+"""
+
+        prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+
+        # Process the PDF if it exists
+        if os.path.exists(file_path):
+            pdf_loader = PyPDFLoader(file_path)
+            pages = pdf_loader.load_and_split()
+            context = "\n".join(str(page.page_content) for page in pages[:30])  # Limit to first 30 pages
+            stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+            stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
+            return stuff_answer['output_text']
+        else:
+            return "Error: Unable to process the document. Please ensure the PDF file is valid."
+    except Exception as e:
+        return f"An error occurred: {e}"  # Generic error handling
+
+# Create a Gradio interface
+interface = Interface(
     fn=initialize,
     inputs=[
-        gr.File(label="Upload PDF", type="file"),
+        gr.File(label="Upload PDF"),  # No need for 'type' argument
         gr.Textbox(label="Question")
     ],
     outputs="text",
@@ -44,8 +58,7 @@ iface = gr.Interface(
 )
 
 # Launch the interface
-iface.launch()
-
+interface.launch()