Update app.py

app.py

@@ -1,44 +1,80 @@
-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"]
+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__, static_folder="static", template_folder="templates")
+# === 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)