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api.py

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+from fastapi import FastAPI
+from langchain.document_loaders import DirectoryLoader, PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+import os
+import uvicorn
+
+# Initialize FastAPI App 🚀
+app = FastAPI(title="Vision Transformer Assistant", description="A FastAPI-powered AI assistant for deep learning.")
+
+# Load Hugging Face Token 🔑
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if not HF_TOKEN:
+    raise ValueError("⚠️ HF_TOKEN is missing! Add it in Hugging Face Secrets.")
+
+# Load Documents 📂
+loader = DirectoryLoader("./data/", glob="*.pdf", loader_cls=PyPDFLoader)
+docs = loader.load()
+
+# Text Splitting 📖
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+texts = text_splitter.split_documents(docs)
+
+# Vector Database 🔍
+db = FAISS.from_documents(documents=texts, embedding=HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
+retriever = db.as_retriever()
+
+# Load LLM 🚀
+repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+llm = HuggingFaceEndpoint(repo_id=repo_id, token=HF_TOKEN, task="text-generation")
+
+# Prompt Template ✨
+prompt_temp = ChatPromptTemplate.from_template("""
+You are an AI assistant specializing in deep learning, specifically Vision Transformers.
+
+<context>
+{context}
+<context>
+
+### Instructions:
+- Extract relevant information only from retrieved documents.
+- Provide concise yet detailed responses.
+- Use LaTeX for equations when necessary.
+- Do not make up answers; respond with *'Information not available in retrieved documents.'* if needed.
+""")
+
+# Create Retrieval Chain ⚡
+document_chain = create_stuff_documents_chain(llm, prompt_temp)
+retrieval_chain = create_retrieval_chain(retriever, document_chain)
+
+def get_response(query: str) -> str:
+    """
+    Get AI-generated response based on query.
+    """
+    response = retrieval_chain.invoke({'input': query})
+    answer = response.get('answer', 'Error: No answer generated.')
+    
+    # Debugging Logs
+    print(f"Query: {query} | Answer: {answer}")  
+    
+    return answer
+
+@app.get("/")
+def home():
+    return {"message": "Vision Transformer Assistant API is running 🚀"}
+
+@app.get("/query")
+def get_answer(query: str):
+    """
+    API endpoint to retrieve AI-generated responses.
+    """
+    try:
+        answer = get_response(query)
+        return {"answer": answer}
+    except Exception as e:
+        print(f"Error: {e}")
+        return {"answer": "Error occurred while processing the request."}
+
+# Run FastAPI
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+

app.py

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+import streamlit as st
+import requests
+
+# FastAPI Backend URL
+FASTAPI_URL = "https://pranavreddy18-research.hf.space"
+
+# Streamlit UI
+st.set_page_config(page_title="Vision Transformer Assistant", page_icon="🤖")
+st.title("Vision Transformer Assistant 🤖")
+st.markdown("Ask anything about deep learning and Vision Transformers!")
+
+# User input
+query = st.text_input("Enter your question:")
+
+if st.button("Get Answer"):
+    if query:
+        with st.spinner("Fetching answer..."):
+            try:
+                response = requests.get(f"{FASTAPI_URL}/query", params={"query": query})
+                
+                # Check if response is valid
+                if response.status_code == 200:
+                    answer = response.json().get("answer", "No answer found.")
+                    st.success("✅ Answer:")
+                    st.write(answer)
+                else:
+                    st.error(f"⚠️ Error fetching answer. Status Code: {response.status_code}")
+                    st.write(response.text)  # Debugging info
+
+            except requests.exceptions.RequestException as e:
+                st.error(f"⚠️ Failed to connect to backend: {e}")
+

requirements.txt

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+fastapi
+uvicorn
+streamlit
+requests
+langchain
+langchain-core
+langchain-huggingface
+sentence-transformers
+pypdf
+faiss-cpu