showcase

app.py

@@ -1,7 +1,77 @@
+import os
 import gradio as gr
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough, RunnableMap
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
 
-def greet(name):
-    return "Hello " + name + "!!"
+load_dotenv()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Load FAISS and RAG
+def load_rag_pipeline():
+    embeddings = HuggingFaceEmbeddings(model_name="pritamdeka/S-PubMedBert-MS-MARCO")
+    db = FAISS.load_local("parkinsons_vector_db", embeddings, allow_dangerous_deserialization=True)
+    retriever = db.as_retriever(search_kwargs={"k": 5})
+
+    template = """You are a Parkinson's disease expert. Follow these rules:
+1. Use {language_style} language (technical/simple)
+2. Base answers ONLY on these sources
+3. Cite sources in your answer
+4. If unsure, say "I don't know"
+
+Sources:
+{context}
+
+Question: {question}
+Answer:"""
+
+    prompt = ChatPromptTemplate.from_template(template)
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
+
+    def build_context(inp):
+        question_text = inp.get("question", "")
+        language_style_text = inp.get("language_style", "")
+
+        docs = retriever.get_relevant_documents(question_text)
+        context = " ".join(doc.page_content for doc in docs)
+
+        return {
+            "question": question_text,
+            "language_style": language_style_text,
+            "context": context
+        }
+
+    rag_chain = (
+        RunnableMap({
+            "question": RunnablePassthrough(),
+            "language_style": RunnablePassthrough()
+        })
+        | build_context
+        | prompt
+        | llm
+    )
+
+    return rag_chain
+
+rag_chain = load_rag_pipeline()
+
+# Gradio Function
+def query_rag(question, language_style):
+    response = rag_chain.invoke({"question": question, "language_style": language_style})
+    return response.content
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=query_rag,
+    inputs=[
+        gr.Textbox(label="Question"),
+        gr.Radio(["simple", "technical"], label="Language Style", value="simple"),
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    title="Parkinson's RAG Assistant",
+    description="Ask questions about Parkinson's disease with simple or technical explanations.",
+)
+
+iface.launch()