Add files via upload

app.py

@@ -0,0 +1,105 @@
+import os
+import validators
+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain_groq import ChatGroq
+from langchain.chains.summarize import load_summarize_chain
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.document_loaders import YoutubeLoader, UnstructuredURLLoader
+import tempfile
+
+# Streamlit App Configuration
+st.set_page_config(page_title="LangChain: Summarize Text From YT, Website, or PDF", page_icon="🦜")
+st.title("🦜 LangChain: Summarize Text From YT, Website, or PDF")
+st.subheader("Summarize Content from a URL or Uploaded PDF")
+
+# Sidebar: API Key Inputs
+with st.sidebar:
+    groq_api_key = st.text_input("Groq API Key", value="", type="password")
+    langsmith_api_key = st.text_input("LangSmith API Key", value="", type="password")  # LangSmith API Key
+
+# Set LangSmith environment variables
+if langsmith_api_key:
+    os.environ["LANGCHAIN_TRACING_V2"] = "true"
+    os.environ["LANGCHAIN_API_KEY"] = langsmith_api_key
+
+# URL Input
+generic_url = st.text_input("URL (YouTube or Website)", label_visibility="collapsed")
+
+# PDF File Uploader
+uploaded_file = st.file_uploader("Upload a PDF File", type=["pdf"])
+
+# Prompt Template for Summarization
+initial_prompt_template = """
+Write a concise summary of the following content:
+Content: {text}
+"""
+initial_prompt = PromptTemplate(template=initial_prompt_template, input_variables=["text"])
+
+# Define the refinement prompt
+refinement_prompt_template = """
+The following is a summary that needs refinement:
+Current Summary: {existing_answer}
+
+We have additional content that can be used to refine the summary:
+Content: {text}
+
+Please refine the current summary to include the new information while maintaining conciseness.
+"""
+refinement_prompt = PromptTemplate(template=refinement_prompt_template, input_variables=["existing_answer", "text"])
+
+# Initialize LLM with Groq API Key
+llm = ChatGroq(model="gemma2-9b-it", groq_api_key=groq_api_key)
+
+# Button to Summarize Content
+if st.button("Summarize the Content"):
+    if not groq_api_key.strip():
+        st.error("Please provide the Groq API Key to get started.")
+    elif not langsmith_api_key.strip():
+        st.error("Please provide the LangSmith API Key for tracking.")
+    elif not (generic_url.strip() or uploaded_file):
+        st.error("Please provide a valid URL or upload a PDF file.")
+    elif generic_url and not validators.url(generic_url):
+        st.error("Please enter a valid URL. It can be a YouTube video or website URL.")
+    else:
+        try:
+            with st.spinner("Processing..."):
+                # Load content from URL (YouTube or Website)
+                if generic_url.strip():
+                    if "youtube.com" in generic_url:
+                        loader = YoutubeLoader.from_youtube_url(generic_url, add_video_info=True)
+                    else:
+                        loader = UnstructuredURLLoader(
+                            urls=[generic_url],
+                            ssl_verify=False,
+                            headers={
+                                "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 13_5_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36"
+                            },
+                        )
+                    docs = loader.load()
+
+                # Load content from uploaded PDF
+                elif uploaded_file:
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+                        temp_file.write(uploaded_file.read())
+                        temp_file_path = temp_file.name
+                    
+                    # Load the PDF using PyPDFLoader
+                    loader = PyPDFLoader(temp_file_path)
+                    docs = loader.load_and_split()
+
+                # Summarize the content with LangSmith tracking enabled
+                chain = load_summarize_chain(
+                    llm,
+                    chain_type="refine",
+                    question_prompt=initial_prompt,
+                    refine_prompt=refinement_prompt,
+                    verbose=True
+                )
+                output_summary = chain.run(docs)
+
+                # Display the summary
+                st.success(output_summary)
+
+        except Exception as e:
+            st.exception(f"Exception: {e}")

requirements.txt

@@ -0,0 +1,21 @@
+langchain
+ipykernel
+langchain-community
+pypdf
+pymupdf
+langchain-text-splitters
+langchain-openai
+sentence_transformers
+langchain_huggingface
+duckdb
+pandas
+openai
+langchain-groq
+duckduckgo_search==5.3.1b1
+pymupdf
+validators==0.28.1
+youtube_transcript_api
+unstructured
+pytube
+numexpr
+huggingface_hub