Create app.py

app.py

@@ -0,0 +1,60 @@
+import os
+import gradio as gr
+import asyncio
+from langchain_core.prompts import PromptTemplate
+from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+from langchain.chains.question_answering import load_qa_chain  # Import load_qa_chain
+
+
+async def initialize(file_path, question):
+    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(f"Page {i+1}: {page.page_content}" for i, page in enumerate(pages[:30]))
+        stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+        # Refactor the below line to make sure it returns an awaitable object
+        stuff_answer = await stuff_chain.arun({"input_documents": pages, "question": question, "context": context})
+        
+        # Extract the page number where the context was found
+        sources = []
+        for i, page in enumerate(pages):
+            if question.lower() in page.page_content.lower():
+                sources.append(f"Page {i+1}")
+
+        if sources:
+            source_str = f" (Source: {', '.join(sources)})"
+        else:
+            source_str = " (Source: Not found in specific page)"
+
+        # Add the clickable link to the source
+        file_name = os.path.basename(file_path)
+        source_link = f"[{file_name}](file://{os.path.abspath(file_path)})"
+        return f"{stuff_answer['output_text']} {source_str} - [Document: {source_link}]"
+    else:
+        return "Error: Unable to process the document. Please ensure the PDF file is valid."
+
+
+# Define Gradio Interface
+input_file = gr.File(label="Upload PDF File")
+input_question = gr.Textbox(label="Ask about the document")
+output_text = gr.Textbox(label="Answer - GeminiPro")
+
+async def pdf_qa(file, question):
+    answer = await initialize(file.name, question)
+    return answer
+
+# Create Gradio Interface
+gr.Interface(fn=pdf_qa, inputs=[input_file, input_question], outputs=output_text, title="PDF Question Answering System", description="Upload a PDF file and ask questions about the content.").launch()