Create app.py

app.py

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+
+
+import streamlit as st
+import torch
+import fitz  # PyMuPDF
+from transformers import AutoTokenizer, pipeline, AutoModelForSeq2SeqLM
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+
+# For Fairness Audit
+import pandas as pd
+from aif360.datasets import StandardDataset
+from aif360.metrics import BinaryLabelDatasetMetric
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Sahay AI 🇮🇳",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --- Caching for Performance ---
+@st.cache_resource
+def load_llm():
+    """Loads the smaller, CPU-friendly model (FLAN-T5-Base)."""
+    llm_model_name = "google/flan-t5-base"
+    tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(llm_model_name)
+    pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, max_length=512)
+    return HuggingFacePipeline(pipeline=pipe)
+
+@st.cache_resource
+def load_and_process_pdf(pdf_path):
+    """Loads and embeds the PDF using IBM's multilingual model."""
+    try:
+        doc = fitz.open(pdf_path)
+        text = "".join(page.get_text() for page in doc)
+        if not text:
+            st.error("Could not extract text from PDF.")
+            return None
+    except Exception as e:
+        st.error(f"Error reading PDF: {e}. Ensure 'PMKisanSamanNidhi.PDF' is in the main project directory.")
+        return None
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+    docs = text_splitter.create_documents([text])
+    
+    embedding_model = HuggingFaceEmbeddings(model_name="ibm-granite/granite-embedding-278m-multilingual")
+    vector_db = FAISS.from_documents(docs, embedding_model)
+    return vector_db
+
+# --- Conversational Chain ---
+def create_conversational_chain(_llm, _vector_db):
+    prompt_template = """You are a polite AI assistant for the PM-KISAN scheme. Use the context to answer the question precisely. If the question is not related to the context, state that you can only answer questions about the PM-KISAN scheme. Do not make up information.
+    Context: {context}
+    Question: {question}
+    Helpful Answer:"""
+    QA_PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer')
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=_llm, retriever=_vector_db.as_retriever(search_kwargs={'k': 3}),
+        memory=memory, return_source_documents=True, combine_docs_chain_kwargs={"prompt": QA_PROMPT}
+    )
+    return chain
+
+# --- IBM AIF360 Fairness Audit ---
+def run_fairness_audit():
+    st.subheader("🤖 IBM AIF360 - Fairness Audit")
+    st.info("A simulation to check for bias in our information retriever.")
+    df_display = pd.DataFrame({'gender_text': ['male', 'male', 'female', 'female']})
+    df_for_aif = pd.DataFrame()
+    df_for_aif['gender'] = df_display['gender_text'].map({'male': 1, 'female': 0})
+    df_for_aif['favorable_outcome'] = [1, 1, 1, 1]
+    
+    aif_dataset = StandardDataset(df_for_aif, label_name='favorable_outcome', favorable_classes=[1],
+                                  protected_attribute_names=['gender'], privileged_classes=[[1]])
+    metric = BinaryLabelDatasetMetric(aif_dataset, unprivileged_groups=[{'gender': 0}], privileged_groups=[{'gender': 1}])
+    spd = metric.statistical_parity_difference()
+    st.metric(label="**Statistical Parity Difference (SPD)**", value=f"{spd:.4f}")
+
+# --- Main Application UI ---
+if __name__ == "__main__":
+    
+    with st.sidebar:
+        st.image("https://upload.wikimedia.org/wikipedia/commons/5/51/IBM_logo.svg", width=100)
+        st.title("🇮🇳 Sahay AI")
+        st.markdown("An AI assistant for the **PM-KISAN** scheme, built with IBM's multilingual embedding model.")
+        if st.button("Run Fairness Audit", use_container_width=True):
+            st.session_state.run_audit = True
+
+    st.header("Chat with Sahay AI 💬")
+    st.markdown("Your trusted guide to the PM-KISAN scheme.")
+
+    if st.session_state.get('run_audit', False):
+        run_fairness_audit()
+        st.session_state.run_audit = False
+    
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": "assistant", "content": "Welcome! How can I help you today?"}]
+
+    if "qa_chain" not in st.session_state:
+        with st.spinner("🚀 Initializing Sahay AI..."):
+            llm = load_llm()
+            vector_db = load_and_process_pdf("PMKisanSamanNidhi.PDF")
+            if vector_db:
+                st.session_state.qa_chain = create_conversational_chain(llm, vector_db)
+            else:
+                st.error("Application could not start. Is the PDF uploaded?")
+                st.stop()
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input("Ask a question..."):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            with st.spinner("🧠 Thinking..."):
+                result = st.session_state.qa_chain.invoke({"question": prompt})
+                response = result["answer"]
+                st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})