Update app.py

i have changed the title from Dynamic PDF Question Answering to Ask My PDF

app.py

@@ -1,75 +1,75 @@
-from langchain_community.document_loaders import PDFPlumberLoader
-from langchain_ollama import OllamaLLM
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_core.prompts import PromptTemplate, FewShotPromptTemplate
-import gradio as gr
-
-def process_pdf(file):
-    try:
-        loader = PDFPlumberLoader(file.name)
-        result = loader.load()
-        splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
-        return splitter.split_documents(result)
-    except Exception as e:
-        return f"Error processing PDF: {str(e)}"
-
-def initialize_llm():
-    return OllamaLLM(model="qwen2", temperature=0.7, base_url="https://alpaca-upright-vulture.ngrok-free.app")
-
-def create_prompt():
-    examples = [
-        {"input": "What is the main topic discussed in the document?", 
-         "output": "The document discusses the concept and details of Neural Networks."},
-        {"input": "Explain the term 'activation function' as used in this document.", 
-         "output": "An activation function in the context of this document refers to a mathematical function applied to neurons' output to introduce non-linearity in the model."}
-    ]
-
-    example_template = PromptTemplate(
-        input_variables=["input", "output"],
-        template="Human: {input}\nAssistant: {output}"
-    )
-
-    return FewShotPromptTemplate(
-        examples=examples,
-        example_prompt=example_template,
-        prefix="You are an AI assistant that provides specific and accurate answers based on the provided document.",
-        suffix="Human: {input}\nAssistant:",
-        input_variables=["input"]
-    )
-
-def generate_answer(chain, user_input):
-    try:
-        response = chain.invoke({"input": user_input})
-        return response
-    except Exception as e:
-        return f"Error generating answer: {str(e)}"
-
-def handle_file(file, user_input):
-    if not file:
-        return "Please upload a PDF document."
-    
-    data = process_pdf(file)
-    if isinstance(data, str): 
-        return data
-
-    llm = initialize_llm()
-    prompt = create_prompt()
-    chain = prompt | llm
-
-    if not user_input.strip():
-        return "Please enter a question."
-
-    return generate_answer(chain, user_input)
-
-interface = gr.Interface(
-    fn=handle_file,
-    inputs=[
-        gr.File(label="Upload PDF"),
-        gr.Textbox(lines=2, placeholder="Enter your question here...")
-    ],
-    outputs=gr.Textbox(label="Answer"),
-    title="Dynamic PDF Question Answering",
-    description="Upload a PDF document and ask questions about its content."
-)
-
-interface.launch()
+from langchain_community.document_loaders import PDFPlumberLoader
+from langchain_ollama import OllamaLLM
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.prompts import PromptTemplate, FewShotPromptTemplate
+import gradio as gr
+
+def process_pdf(file):
+    try:
+        loader = PDFPlumberLoader(file.name)
+        result = loader.load()
+        splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
+        return splitter.split_documents(result)
+    except Exception as e:
+        return f"Error processing PDF: {str(e)}"
+
+def initialize_llm():
+    return OllamaLLM(model="qwen2", temperature=0.7, base_url="https://alpaca-upright-vulture.ngrok-free.app")
+
+def create_prompt():
+    examples = [
+        {"input": "What is the main topic discussed in the document?", 
+         "output": "The document discusses the concept and details of Neural Networks."},
+        {"input": "Explain the term 'activation function' as used in this document.", 
+         "output": "An activation function in the context of this document refers to a mathematical function applied to neurons' output to introduce non-linearity in the model."}
+    ]
+
+    example_template = PromptTemplate(
+        input_variables=["input", "output"],
+        template="Human: {input}\nAssistant: {output}"
+    )
+
+    return FewShotPromptTemplate(
+        examples=examples,
+        example_prompt=example_template,
+        prefix="You are an AI assistant that provides specific and accurate answers based on the provided document.",
+        suffix="Human: {input}\nAssistant:",
+        input_variables=["input"]
+    )
+
+def generate_answer(chain, user_input):
+    try:
+        response = chain.invoke({"input": user_input})
+        return response
+    except Exception as e:
+        return f"Error generating answer: {str(e)}"
+
+def handle_file(file, user_input):
+    if not file:
+        return "Please upload a PDF document."
+    
+    data = process_pdf(file)
+    if isinstance(data, str): 
+        return data
+
+    llm = initialize_llm()
+    prompt = create_prompt()
+    chain = prompt | llm
+
+    if not user_input.strip():
+        return "Please enter a question."
+
+    return generate_answer(chain, user_input)
+
+interface = gr.Interface(
+    fn=handle_file,
+    inputs=[
+        gr.File(label="Upload PDF"),
+        gr.Textbox(lines=2, placeholder="Enter your question here...")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Ask My PDF",
+    description="Upload a PDF document and ask questions about its content."
+)
+
+interface.launch()