Create app.py

app.py

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+import os
+import kagglehub
+import gradio as gr
+import pandas as pd
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.documents import Document
+from langchain_classic.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain_classic.chains.combine_documents import create_stuff_documents_chain
+
+# ==========================================
+# SYSTEM SETUP
+# ==========================================
+# In HF Spaces, use os.getenv to get the secret
+api_key = os.getenv("GOOGLE_API_KEY")
+os.environ["GOOGLE_API_KEY"] = api_key
+
+# Load Dataset
+print("📥 Initializing Legal Database...")
+path = kagglehub.dataset_download("muhammadahmad246/labeled-legal-cases-for-supreme-court-of-pakistan")
+
+all_docs = []
+for root, _, files in os.walk(path):
+    for f in files:
+        if f.endswith('.txt') and len(all_docs) < 150:
+            try:
+                with open(os.path.join(root, f), 'r', encoding='utf-8', errors='ignore') as file:
+                    content = file.read()
+                    if len(content.strip()) > 100:
+                        all_docs.append(Document(page_content=content, metadata={"source": f}))
+            except: continue
+
+# Vector Store
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+chunks = text_splitter.split_documents(all_docs)
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vector_db = Chroma.from_documents(chunks, embeddings)
+retriever = vector_db.as_retriever(search_kwargs={"k": 3})
+
+# AI Setup
+llm = ChatGoogleGenerativeAI(model="models/gemini-2.5-flash", temperature=0.1)
+
+context_p = ChatPromptTemplate.from_messages([
+    ("system", "Formulate a standalone legal search query based on chat history."),
+    MessagesPlaceholder("chat_history"),
+    ("human", "{input}"),
+])
+history_retriever = create_history_aware_retriever(llm, retriever, context_p)
+
+qa_p = ChatPromptTemplate.from_messages([
+    ("system", "You are the Pakistan Supreme Court AI. Context:\n\n{context}"),
+    MessagesPlaceholder("chat_history"),
+    ("human", "{input}"),
+])
+qa_chain = create_stuff_documents_chain(llm, qa_p)
+rag_chain = create_retrieval_chain(history_retriever, qa_chain)
+
+# ==========================================
+# PROFESSIONAL PINK THEME & UI
+# ==========================================
+# Custom theme: Soft Pink and Slate Gray
+custom_theme = gr.themes.Soft(
+    primary_hue="pink",
+    secondary_hue="slate",
+    neutral_hue="rose",
+    font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "system-ui", "sans-serif"]
+)
+
+history_data = []
+
+def chat_fn(message, history):
+    global history_data
+    try:
+        response = rag_chain.invoke({"input": message, "chat_history": history_data})
+        history_data.append(HumanMessage(content=message))
+        history_data.append(AIMessage(content=response["answer"]))
+        if len(history_data) > 6: history_data = history_data[-6:]
+        return response["answer"]
+    except Exception as e:
+        if "429" in str(e): return "🕒 System busy. Please wait 60 seconds."
+        return f"Error: {str(e)}"
+
+# Build UI with Muhammad Bilal's branding
+with gr.Blocks(theme=custom_theme) as demo:
+    gr.Markdown(
+        """
+        # ⚖️ Pakistan Supreme Court Legal AI
+        ### Developed by: **Muhammad Bilal** | Aspiring AI Developer
+        ---
+        *Advanced RAG-based analysis of judicial precedents and constitutional law.*
+        """
+    )
+    
+    chatbot = gr.ChatInterface(
+        fn=chat_fn,
+        examples=["What are the grounds for a Review Petition?", "Summarize Article 10-A.", "Who is Muhammad Bilal?"],
+        cache_examples=False
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        *Note: This AI is for research purposes. Always consult a legal professional for actual court matters.*
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()