Update app.py

app.py

@@ -1,83 +1,44 @@
-from dotenv import load_dotenv
-load_dotenv()
-
-import os
-from flask import Flask, request, jsonify, render_template
-from flask_cors import CORS
-
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import Chroma
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_core.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-
-# === Step 1: Load API Key ===
-GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    raise ValueError("GOOGLE_API_KEY not found in environment variables.")
-
-# === Step 2: Initialize LLM (Gemini) ===
-llm = ChatGoogleGenerativeAI(
-    model="gemini-2.0-flash-lite",
-    google_api_key=GOOGLE_API_KEY,
-    convert_system_message_to_human=True
-)
-
-# === Step 3: Load Chroma Vector Store ===
-embedding_model = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
-vectordb = Chroma(
-    persist_directory="./chroma_store",
-    embedding_function=embedding_model,
-    collection_name="pdf_search_chroma"
-)
-retriever = vectordb.as_retriever(search_kwargs={"k": 6})
-
-# === Step 4: Custom Prompt Template ===
-prompt_template = PromptTemplate.from_template("""
-You are an intelligent assistant for students asking about their university.
-If answer is not defined or not clearly understood, ask for clarification.
-Answer clearly and helpfully based on the retrieved context. Do not make up information or suggestions.
-
-Context:
-{context}
-
-Question:
-{question}
-
-Answer:
-""")
-
-# === Step 5: Create Retrieval-QA Chain ===
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=retriever,
-    chain_type_kwargs={"prompt": prompt_template}
-)
-
-# === Step 6: Flask Setup ===
-app = Flask(__name__)
-# === Step 7: Serve Frontend ===
-
-@app.route("/")
-def index():
-    return render_template("index.html")  # Make sure chat.html exists
-
-    
-@app.route('/get', methods=['POST'])
-def chat():
-    data = request.json
-    query = data.get('message', '').strip()
-
-    if not query:
-        return jsonify({"error": "No message provided."}), 400
-
-    try:
-        response = qa_chain.run(query)
-        return jsonify({"response": response})
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-# === Step 9: Run the App ===
-if __name__ == '__main__':
-    app.run(debug=False, host='0.0.0.0', port=7860)
+FROM python:3.9
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set working directory
+WORKDIR /app
+
+# Copy and install Python dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Install system dependencies and build tools
+USER root
+RUN apt-get update && apt-get install -y \
+    python3 \
+    python3-pip \
+    python3-venv \
+    wget \
+    build-essential
+
+# Install newer SQLite version
+WORKDIR /tmp
+RUN wget https://www.sqlite.org/2023/sqlite-autoconf-3410200.tar.gz \
+    && tar -xvf sqlite-autoconf-3410200.tar.gz \
+    && cd sqlite-autoconf-3410200 \
+    && ./configure \
+    && make \
+    && make install
+
+# Update library path to use the new SQLite
+ENV LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+
+# Go back to non-root user and app directory
+USER user
+WORKDIR /app
+
+# Copy the app source code
+COPY --chown=user . /app
+
+# Start the app
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app", "--preload"]