Update app.py

app.py

@@ -1,172 +1,531 @@
 # =======================================
-# 📘 RAG App Pro – Gemini + Smart Embeddings (Multi-User Safe)
+# 📘 RAG App Pro – Groq + Smart Embeddings
+#    Multi-User Safe
 # =======================================
 
-import os, re, shutil, textwrap, requests, uuid
+import os
+import re
+import requests
+import uuid
+
 from bs4 import BeautifulSoup
-import google.generativeai as genai
+from groq import Groq
+
 from sentence_transformers import SentenceTransformer
 import chromadb
 import gradio as gr
+
 from langchain_community.document_loaders import UnstructuredPDFLoader
 import camelot
 
+
 # ======================
-# 🔹 Gemini + Local Setup
+# 🔹 Groq + Local Setup
 # ======================
-genai.configure(api_key="AQ.Ab8RN6I4za-ifPTOLhC78wesRWQxGaWeQQZZa44Cv7dBtaxX6A")  # 🔑 apni Gemini key
-MODEL = "gemini-3.5-flash"
 
+# Set your Groq API key as an environment variable:
+# Windows CMD:
+# set GROQ_API_KEY=your_groq_api_key
+#
+# PowerShell:
+# $env:GROQ_API_KEY="your_groq_api_key"
+
+GROQ_API_KEY = os.getenv("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")
+
+if not GROQ_API_KEY:
+    raise ValueError(
+        "GROQ_API_KEY not found. Please set your Groq API key "
+        "as an environment variable."
+    )
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# Groq model
+MODEL = "llama-3.3-70b-versatile"
+
+# Local embedding model
 embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# ChromaDB
 chroma_client = chromadb.Client()
 
+
 # ======================
 # 🔹 Utils
 # ======================
+
 def clean_text(text):
     return re.sub(r"\s+", " ", text).strip()
 
+
 def adaptive_chunk_text(text):
     length = len(text)
+
     if length < 3000:
         size = 500
     elif length < 10000:
         size = 1000
     else:
         size = 1500
+
     chunks = []
+
     for i in range(0, len(text), size - 150):
         chunks.append(text[i:i + size])
+
     return chunks
 
+
 def extract_pdf_text(pdf_path):
     """Smart PDF extractor (tables + text)"""
+
     full_text = ""
+
+    # ----------------------
+    # Extract tables
+    # ----------------------
     try:
-        tables = camelot.read_pdf(pdf_path, pages="all")
+        tables = camelot.read_pdf(
+            pdf_path,
+            pages="all"
+        )
+
         for i, table in enumerate(tables):
-            full_text += f"\n\n[Table {i+1}]\n" + table.df.to_string(index=False)
+            full_text += (
+                f"\n\n[Table {i + 1}]\n"
+                + table.df.to_string(index=False)
+            )
+
     except Exception:
         pass
 
+    # ----------------------
+    # Extract normal text
+    # ----------------------
     try:
         loader = UnstructuredPDFLoader(pdf_path)
+
         docs = loader.load()
-        full_text += "\n\n".join([doc.page_content for doc in docs])
+
+        full_text += "\n\n".join(
+            [doc.page_content for doc in docs]
+        )
+
     except Exception as e:
-        full_text += f"\n\n[Error extracting text: {e}]"
+        full_text += (
+            f"\n\n[Error extracting text: {e}]"
+        )
 
     return clean_text(full_text)
 
+
 # ======================
 # 🔹 Session Handling
 # ======================
+
 def create_user_collection():
-    """Each user/session gets unique collection"""
+
+    """Each user/session gets a unique collection"""
+
     session_id = f"user_{str(uuid.uuid4())[:8]}"
-    collection = chroma_client.create_collection(name=session_id)
+
+    collection = chroma_client.create_collection(
+        name=session_id
+    )
+
     return session_id, collection
 
+
 def reset_collection(collection_name):
-    """Delete previous data for same user"""
+
+    """Delete previous user data"""
+
     try:
-        chroma_client.delete_collection(name=collection_name)
+        chroma_client.delete_collection(
+            name=collection_name
+        )
+
     except Exception:
         pass
-    return chroma_client.create_collection(name=collection_name)
+
+    return chroma_client.create_collection(
+        name=collection_name
+    )
+
 
 # ======================
 # 🔹 Ingestion Logic
 # ======================
+
 def ingest_source(source, from_url, collection_name):
+
     # Delete previous user data
-    collection = reset_collection(collection_name)
+    collection = reset_collection(
+        collection_name
+    )
 
+    # ----------------------
+    # Website
+    # ----------------------
     if from_url:
-        html = requests.get(source, timeout=15).text
-        soup = BeautifulSoup(html, "html.parser")
-        text = clean_text(soup.get_text())
+
+        try:
+            response = requests.get(
+                source,
+                timeout=15,
+                headers={
+                    "User-Agent": "Mozilla/5.0"
+                }
+            )
+
+            response.raise_for_status()
+
+            soup = BeautifulSoup(
+                response.text,
+                "html.parser"
+            )
+
+            # Remove unnecessary HTML
+            for tag in soup(
+                ["script", "style", "noscript"]
+            ):
+                tag.decompose()
+
+            text = clean_text(
+                soup.get_text()
+            )
+
+        except Exception as e:
+
+            return f"⚠️ Website error: {e}"
+
+    # ----------------------
+    # PDF
+    # ----------------------
     else:
-        text = extract_pdf_text(source)
+
+        try:
+            text = extract_pdf_text(source)
+
+        except Exception as e:
+
+            return f"⚠️ PDF error: {e}"
+
+    # ----------------------
+    # Check text
+    # ----------------------
 
     if not text.strip():
-        return "⚠️ No readable text found (maybe image-only PDF)."
+
+        return (
+            "⚠️ No readable text found "
+            "(maybe image-only PDF)."
+        )
+
+    # ----------------------
+    # Chunking
+    # ----------------------
 
     chunks = adaptive_chunk_text(text)
-    embeddings = embedder.encode(chunks).tolist()
+
+    # ----------------------
+    # Embeddings
+    # ----------------------
+
+    embeddings = embedder.encode(
+        chunks
+    ).tolist()
+
+    # ----------------------
+    # Store in ChromaDB
+    # ----------------------
 
     for i, emb in enumerate(embeddings):
-        collection.add(ids=[f"{collection_name}_{i}"], embeddings=[emb], documents=[chunks[i]])
 
-    return f"✅ [{collection_name}] Ingested {len(chunks)} chunks successfully!"
+        collection.add(
+            ids=[
+                f"{collection_name}_{i}"
+            ],
+            embeddings=[emb],
+            documents=[chunks[i]]
+        )
+
+    return (
+        f"✅ [{collection_name}] "
+        f"Ingested {len(chunks)} chunks successfully!"
+    )
+
 
 # ======================
 # 🔹 Query Logic
 # ======================
+
 def rag_query(query, collection_name):
+
     try:
-        collection = chroma_client.get_collection(name=collection_name)
-        q_emb = embedder.encode([query]).tolist()
-        results = collection.query(query_embeddings=q_emb, n_results=4)
-        if not results["documents"]:
-            return "⚠️ No context found. Try ingesting data first."
 
-        context = "\n\n".join(results["documents"][0])
-        prompt = f"""
-You are a knowledgeable AI assistant.
-Use the context below to answer clearly and in multiple lines.
+        # ----------------------
+        # Get collection
+        # ----------------------
+
+        collection = chroma_client.get_collection(
+            name=collection_name
+        )
+
+        # ----------------------
+        # Embed user query
+        # ----------------------
+
+        q_emb = embedder.encode(
+            [query]
+        ).tolist()
+
+        # ----------------------
+        # Semantic search
+        # ----------------------
+
+        results = collection.query(
+            query_embeddings=q_emb,
+            n_results=4
+        )
 
+        if (
+            not results["documents"]
+            or not results["documents"][0]
+        ):
+            return (
+                "⚠️ No context found. "
+                "Try ingesting data first."
+            )
+
+        # ----------------------
+        # Build context
+        # ----------------------
+
+        context = "\n\n".join(
+            results["documents"][0]
+        )
+
+        # ----------------------
+        # Groq prompt
+        # ----------------------
+
+        system_prompt = """
+You are a knowledgeable RAG assistant.
+
+Answer the user's question using ONLY
+the provided context.
+
+Rules:
+1. Do not invent information.
+2. If the answer is not present in the context,
+   clearly say that the information was not found.
+3. Give a clear and useful answer.
+4. Use multiple lines when appropriate.
+5. Keep the answer concise but informative.
+"""
+
+        user_prompt = f"""
 Context:
+
 {context}
 
-Question: {query}
-Answer:
+Question:
+
+{query}
 """
-        response = genai.GenerativeModel(MODEL).generate_content(prompt)
-        ans = response.text.replace(". ", ".\n")
-        return ans
+
+        # ----------------------
+        # Groq API
+        # ----------------------
+
+        response = client.chat.completions.create(
+
+            model=MODEL,
+
+            messages=[
+                {
+                    "role": "system",
+                    "content": system_prompt
+                },
+                {
+                    "role": "user",
+                    "content": user_prompt
+                }
+            ],
+
+            temperature=0.2,
+
+            max_completion_tokens=1000
+        )
+
+        # ----------------------
+        # Get answer
+        # ----------------------
+
+        answer = response.choices[
+            0
+        ].message.content
+
+        return answer
+
     except Exception as e:
+
         return f"⚠️ Error: {e}"
 
+
 # ======================
-# 🔹 Gradio UI
+# 🔹 Session
 # ======================
+
 def start_new_session():
+
     session_id, _ = create_user_collection()
+
     return session_id
 
+
 session_id = start_new_session()
 
+
+# ======================
+# 🔹 Gradio Functions
+# ======================
+
 def ingest_website(url):
-    return ingest_source(url, True, session_id)
+
+    return ingest_source(
+        url,
+        True,
+        session_id
+    )
+
 
 def ingest_pdf(file):
-    return ingest_source(file.name, False, session_id)
+
+    if file is None:
+        return "⚠️ Please upload a PDF."
+
+    return ingest_source(
+        file.name,
+        False,
+        session_id
+    )
+
 
 def query_ask(q):
-    return rag_query(q, session_id)
 
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald")) as demo:
-    gr.Markdown("# 🤖 Smart RAG App Pro (Gemini + Adaptive PDF + Multi-User Mode)")
+    if not q.strip():
+        return "⚠️ Please enter a question."
+
+    return rag_query(
+        q,
+        session_id
+    )
+
+
+# ======================
+# 🔹 Gradio UI
+# ======================
+
+with gr.Blocks(
+    theme=gr.themes.Soft(
+        primary_hue="emerald"
+    )
+) as demo:
+
+    gr.Markdown(
+        "# 🤖 Smart RAG App Pro "
+        "(Groq + Adaptive PDF + Multi-User Mode)"
+    )
+
+    gr.Markdown(
+        f"""
+🆕 **Private Session ID:** `{session_id}`
 
-    gr.Markdown(f"🆕 **Private Session ID:** `{session_id}` – Your data is isolated and auto-clears on refresh.")
+Your uploaded/ingested data is isolated
+inside your session.
+"""
+    )
+
+    # ======================
+    # Website Tab
+    # ======================
 
     with gr.Tab("🌐 Ingest Website"):
-        url_in = gr.Textbox(label="Enter Website URL")
-        url_btn = gr.Button("Ingest Website")
-        url_out = gr.Textbox(label="Status")
-        url_btn.click(fn=ingest_website, inputs=url_in, outputs=url_out)
+
+        url_in = gr.Textbox(
+            label="Enter Website URL",
+            placeholder="https://example.com"
+        )
+
+        url_btn = gr.Button(
+            "Ingest Website"
+        )
+
+        url_out = gr.Textbox(
+            label="Status"
+        )
+
+        url_btn.click(
+            fn=ingest_website,
+            inputs=url_in,
+            outputs=url_out
+        )
+
+    # ======================
+    # PDF Tab
+    # ======================
 
     with gr.Tab("📄 Ingest PDF"):
-        pdf_in = gr.File(label="Upload PDF")
-        pdf_btn = gr.Button("Ingest PDF")
-        pdf_out = gr.Textbox(label="Status")
-        pdf_btn.click(fn=ingest_pdf, inputs=pdf_in, outputs=pdf_out)
+
+        pdf_in = gr.File(
+            label="Upload PDF",
+            file_types=[".pdf"]
+        )
+
+        pdf_btn = gr.Button(
+            "Ingest PDF"
+        )
+
+        pdf_out = gr.Textbox(
+            label="Status"
+        )
+
+        pdf_btn.click(
+            fn=ingest_pdf,
+            inputs=pdf_in,
+            outputs=pdf_out
+        )
+
+    # ======================
+    # Ask Tab
+    # ======================
 
     with gr.Tab("💬 Ask Questions"):
-        q_in = gr.Textbox(label="Ask anything from ingested data")
-        q_btn = gr.Button("Ask Gemini")
-        q_out = gr.Markdown(label="Answer")
-        q_btn.click(fn=query_ask, inputs=q_in, outputs=q_out)
 
-demo.launch()
+        q_in = gr.Textbox(
+            label="Ask anything from ingested data",
+            placeholder="Ask a question..."
+        )
+
+        q_btn = gr.Button(
+            "Ask Groq"
+        )
+
+        q_out = gr.Markdown(
+            label="Answer"
+        )
+
+        q_btn.click(
+            fn=query_ask,
+            inputs=q_in,
+            outputs=q_out
+        )
+
+
+# ======================
+# 🔹 Launch
+# ======================
+
+demo.launch()