Update app.py

app.py

@@ -1,17 +1,12 @@
 import streamlit as st
 import os
 import requests
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langdetect import detect
 
 # Load the Hugging Face token from environment variables (secrets)
 token = os.environ.get("KEY2")  # Replace "KEY2" with your secret key name
 
-# Initialize the Hugging Face Inference API
-def query_huggingface_api(prompt, max_new_tokens=200, temperature=0.7, top_k=50):
+# Function to query the Hugging Face API
+def query_huggingface_api(prompt, max_new_tokens=50, temperature=0.7, top_k=50):
     model_name = "HuggingFaceH4/zephyr-7b-alpha"  # Replace with your preferred model
     api_url = f"https://api-inference.huggingface.co/models/{model_name}"
     headers = {"Authorization": f"Bearer {token}"}
@@ -30,117 +25,20 @@ def query_huggingface_api(prompt, max_new_tokens=200, temperature=0.7, top_k=50)
         st.error(f"Error: {response.status_code} - {response.text}")
         return None
 
-# Extract text from PDF
-def extract_text_from_pdf(file):
-    reader = PdfReader(file)
-    text = ""
-    for page in reader.pages:
-        text += page.extract_text()
-    return text
-
-# Split text into chunks
-def split_text(text, chunk_size=1000, chunk_overlap=200):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
-    chunks = splitter.split_text(text)
-    return chunks
-
-# Create embeddings and vector store
-def create_vector_store(chunks, indexing_method="multi-representation", **kwargs):
-    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-    if indexing_method == "multi-representation":
-        vector_store = FAISS.from_texts(chunks, embeddings)
-    elif indexing_method == "raptors":
-        # Implement RAPTORS logic here (e.g., hierarchical chunking)
-        vector_store = FAISS.from_texts(chunks, embeddings)
-    elif indexing_method == "colbert":
-        # Implement ColBERT logic here (e.g., contextualized embeddings)
-        vector_store = FAISS.from_texts(chunks, embeddings)
-    return vector_store
-
-# Query the PDF using the Hugging Face API
-def query_pdf(vector_store, query, query_method="multi-query", max_new_tokens=200, temperature=0.7, top_k=50):
-    # Retrieve relevant chunks from the vector store
-    docs = vector_store.similarity_search(query)
-    context = " ".join([doc.page_content for doc in docs])
-
-    # Create a prompt for the LLM
-    prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
-
-    # Query the Hugging Face API
-    answer = query_huggingface_api(prompt, max_new_tokens=max_new_tokens, temperature=temperature, top_k=top_k)
-    return answer, docs
-
-# Detect language of the text
-def detect_language(text):
-    try:
-        return detect(text)
-    except:
-        return "en"  # Default to English if detection fails
-
 # Streamlit App
 def main():
-    st.title("Chat with PDF")
-    st.write("Upload a PDF and ask questions about it!")
-
-    # File uploader
-    uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
-    if uploaded_file is None:
-        st.info("Using default PDF.")
-        uploaded_file = "default.pdf"  # Add a default PDF
-
-    # Step 1: Extract text and split into chunks
-    if "text" not in st.session_state:
-        st.session_state.text = None
-    if "chunks" not in st.session_state:
-        st.session_state.chunks = None
-
-    if st.button("Extract Text and Split into Chunks"):
-        st.session_state.text = extract_text_from_pdf(uploaded_file)
-        st.session_state.chunks = split_text(st.session_state.text)
-        st.success("Text extracted and split into chunks!")
-
-    # Step 2: Create vector store
-    if "vector_store" not in st.session_state:
-        st.session_state.vector_store = None
-
-    if st.session_state.chunks:
-        st.subheader("Indexing Options")
-        indexing_method = st.selectbox(
-            "Indexing Method",
-            ["multi-representation", "raptors", "colbert"],
-            help="Choose how to index the PDF text."
-        )
-        if st.button("Create Vector Store"):
-            st.session_state.vector_store = create_vector_store(st.session_state.chunks, indexing_method=indexing_method)
-            st.success("Vector store created!")
-
-    # Step 3: Query the PDF
-    if st.session_state.vector_store:
-        st.subheader("Query Translation Options")
-        query_method = st.selectbox(
-            "Query Translation Method",
-            ["multi-query", "rag-fusion", "decomposition", "step-back", "hyde"],
-            help="Choose a method to improve query retrieval."
-        )
-        st.subheader("LLM Parameters")
-        temperature = st.slider("Temperature", 0.1, 1.0, 0.7, help="Controls randomness in the output.")
-        top_k = st.slider("Top-k", 1, 100, 50, help="Limits sampling to the top-k tokens.")
-        max_new_tokens = st.slider("Max New Tokens", 50, 500, 200, help="Maximum number of tokens to generate.")
-        query = st.text_input("Ask a question about the PDF:")
-        if query:
-            answer, source_docs = query_pdf(
-                st.session_state.vector_store,
-                query,
-                query_method=query_method,
-                max_new_tokens=max_new_tokens,
-                temperature=temperature,
-                top_k=top_k,
-            )
-            if answer:
-                st.write("**Answer:**", answer)
-                st.write("**Source Text:**")
-                for doc in source_docs:
-                    st.write(doc.page_content)
+    st.title("Hugging Face API Test")
+    st.write("Enter a prompt and get a response from the model.")
+
+    # Input prompt
+    prompt = st.text_input("Enter your prompt:")
+    if prompt:
+        st.write("**Prompt:**", prompt)
+
+        # Query the Hugging Face API
+        response = query_huggingface_api(prompt)
+        if response:
+            st.write("**Response:**", response)
 
 if __name__ == "__main__":
     main()