Create app.py

app.py

@@ -0,0 +1,171 @@
+import os
+import gradio as gr
+import PyPDF2
+import requests
+
+# ================= CONFIG =================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "")
+GROQ_MODEL = "llama-3.1-8b-instant"
+GROQ_URL = "https://api.groq.com/openai/v1/chat/completions"
+
+processed_texts = {}
+
+# ================= PDF HANDLING =================
+def extract_pdf_text(file):
+    reader = PyPDF2.PdfReader(file)
+    text = ""
+    for page in reader.pages:
+        text += (page.extract_text() or "") + "\n"
+    return text
+
+
+def chunk_text(text, chunk_size=400, overlap=50):
+    words = text.split()
+    chunks = []
+    i = 0
+    while i < len(words):
+        chunk = " ".join(words[i:i + chunk_size])
+        chunks.append(chunk)
+        i += chunk_size - overlap
+    return chunks
+
+
+def preview_documents(files):
+    processed_texts.clear()
+    rows = []
+
+    for f in files:
+        text = extract_pdf_text(f)
+        name = os.path.basename(f)
+
+        chunks = chunk_text(text)
+        processed_texts[name] = chunks
+
+        rows.append([
+            name,
+            len(text.split()),
+            text[:300],
+            f"{len(chunks)} chunks"
+        ])
+
+    return rows
+
+
+def process_documents(files):
+    if not processed_texts:
+        return "❌ Preview documents first."
+    return f"✅ {len(processed_texts)} document(s) processed."
+
+
+# ================= GROQ CALL =================
+def query_groq(prompt):
+    if not GROQ_API_KEY:
+        return "❌ GROQ_API_KEY not set."
+
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": GROQ_MODEL,
+        "messages": [
+            {"role": "system", "content": "Answer ONLY from the provided documents."},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.1,
+        "max_tokens": 400
+    }
+
+    r = requests.post(GROQ_URL, headers=headers, json=payload, timeout=30)
+
+    if r.status_code == 200:
+        return r.json()["choices"][0]["message"]["content"]
+
+    return f"❌ Groq Error {r.status_code}: {r.text}"
+
+
+# ================= RAG =================
+def retrieve_context(question):
+    question_words = set(question.lower().split())
+    best_chunk = ""
+    best_score = 0
+
+    for chunks in processed_texts.values():
+        for chunk in chunks:
+            chunk_words = set(chunk.lower().split())
+            score = len(question_words & chunk_words)
+            if score > best_score:
+                best_score = score
+                best_chunk = chunk
+
+    return best_chunk[:1000] if best_chunk else ""
+
+
+def answer_question(question, history):
+    if history is None:
+        history = []
+
+    # 🔹 STEP 1: show user message instantly
+    history.append((question, ""))
+
+    if not processed_texts:
+        history[-1] = (question, "⚠️ Upload and process PDFs first.")
+        return history, ""
+
+    context = retrieve_context(question)
+
+    if not context:
+        history[-1] = (question, "❌ No relevant information found in documents.")
+        return history, ""
+
+    prompt = f"""
+DOCUMENT CONTEXT:
+{context}
+
+QUESTION:
+{question}
+
+Answer clearly using the document context only.
+"""
+
+    # 🔹 STEP 2: get model response
+    answer = query_groq(prompt)
+
+    # 🔹 STEP 3: replace last empty reply
+    history[-1] = (question, answer)
+
+    return history, ""
+
+
+# ================= UI =================
+with gr.Blocks(title="RAG PDF Chatbot") as demo:
+    gr.Markdown("# 📚 RAG PDF Chatbot (Groq)")
+    gr.Markdown("*Upload PDFs → Preview → Ask questions*")
+
+    with gr.Row():
+        files = gr.File(file_types=[".pdf"], file_count="multiple")
+        preview_btn = gr.Button("📄 Preview")
+        process_btn = gr.Button("🚀 Process")
+        status = gr.Textbox(label="Status")
+
+    table = gr.DataFrame(
+        headers=["File", "Words", "Preview", "Chunks"],
+        interactive=False
+    )
+
+    chatbot = gr.Chatbot(height=420)
+
+    msg = gr.Textbox(
+        placeholder="Ask a question from the documents...",
+        lines=2
+    )
+    send = gr.Button("Send")
+
+    preview_btn.click(preview_documents, files, table)
+    process_btn.click(process_documents, files, status)
+
+    send.click(answer_question, [msg, chatbot], [chatbot, msg])
+    msg.submit(answer_question, [msg, chatbot], [chatbot, msg])
+
+demo.launch()