Initial Commit

.github/workflows/sync_with_hf.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to Hugging face hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://rahul-bhoyar-1995:$HF_TOKEN@huggingface.co/spaces/rahul-bhoyar-1995/Webpage-Querier main

.gitignore

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+venv/

app.py

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+import os
+import streamlit as st
+from llama_index import VectorStoreIndex, download_loader, ServiceContext
+from llama_index.embeddings import HuggingFaceEmbedding
+from llama_index.llms import Gemini, HuggingFaceInferenceAPI, OpenAI
+
+# Create Streamlit web app
+def main():
+    st.title("Webpage Querier by Rahul Bhoyar")
+    # Sidebar for customizations
+    with st.sidebar:
+        st.subheader("Customize Settings")
+        loader = download_loader("BeautifulSoupWebReader")()
+        hf_token = st.text_input("Enter your Hugging Face token:")
+        llm = HuggingFaceInferenceAPI(model_name="HuggingFaceH4/zephyr-7b-alpha", token=hf_token)
+      
+    # Main content area
+    st.markdown("Query your Web page data with using this chatbot")
+
+    # User input: Web page link
+    url = st.text_input("Enter the URL of the web page:")
+
+    # Create Service Context
+    embed_model_uae = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+    service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20, embed_model=embed_model_uae)
+
+    # Load documents
+    if url:
+        documents = loader.load_data(urls=[url])
+        st.success("Documents loaded successfully!")
+        with st.spinner('Creating Vector Embeddings...'):
+            # Create Vector Store Index
+            index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)
+
+            # Persist Storage Context
+            index.storage_context.persist()
+
+            # Create Query Engine
+            query_engine = index.as_query_engine()
+
+        # User input: Query
+        query = st.text_input("Ask a question:")
+        if query:
+            # Run Query
+            response = query_engine.query(query)
+
+            # Display Result
+            st.markdown(f"**Response:** {response}")
+    else:
+        st.warning("Please enter a valid URL.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+llama-index
+streamlit
+transformers[torch]
+huggingface_hub[inference]
+transformers
+beautifulsoup4
+unstructured
+watchdog
+transformers