Update app.py

app.py

@@ -0,0 +1,55 @@
+import os
+from langchain import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.document_loaders import PyPDFLoader
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+import gradio as gr 
+
+import os
+import gradio as gr
+from langchain import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.document_loaders import PyPDFLoader
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Function for initialisation
+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(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."
+
+def chatbot(file):
+    file_path = file.name
+    question_input = file.read()
+    return initialize(file_path, question_input)
+
+# Create Gradio interface
+inputs = gr.inputs.File(label="Upload PDF")
+outputs = gr.outputs.Textbox(label="Answer - GeminiPro")
+
+gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="GeminiPro Q&A Bot", description="Ask questions about the uploaded PDF document.").launch()
+
+
+