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app.py

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+import gradio as gr
+from rapidfuzz import fuzz
+import fitz
+import easyocr
+import numpy as np
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+
+# ===== GLOBALS =====
+reader = easyocr.Reader(['en'])
+db = None
+
+
+# ===== PROCESS PDF =====
+def process_pdf(file):
+    global db
+
+    doc = fitz.open(file.name)
+    text = ""
+
+    for i, page in enumerate(doc[:50]):
+        page_text = page.get_text()
+
+        if len(page_text.strip()) < 50:
+            pix = page.get_pixmap()
+            img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(
+                pix.height, pix.width, pix.n
+            )
+            result = reader.readtext(img)
+            page_text = " ".join([r[1] for r in result])
+
+        text += page_text + "\n"
+
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,
+        chunk_overlap=100
+    )
+    chunks = splitter.split_text(text)
+
+    embeddings = HuggingFaceEmbeddings()
+    db = FAISS.from_texts(chunks, embeddings)
+
+    return "✅ PDF processed! Ask your question now."
+
+
+# ===== ANSWER FUNCTION =====
+def get_answer(query):
+    global db
+
+    if db is None:
+        return "⚠️ Upload and process a PDF first.", ""
+
+    docs = db.similarity_search(query, k=3)
+
+    best_sentence = ""
+    best_score = 0
+    source = ""
+
+    for doc in docs:
+        sentences = doc.page_content.split(".")
+
+        for sent in sentences:
+            sent_clean = sent.strip()
+
+            if len(sent_clean) < 20:
+                continue
+
+            score = fuzz.partial_ratio(query.lower(), sent_clean.lower())
+
+            if "is" in sent_clean.lower() or "mode" in sent_clean.lower():
+                score += 10
+
+            if score > best_score:
+                best_score = score
+                best_sentence = sent_clean
+                source = doc.page_content[:200]
+
+    return best_sentence, source
+
+
+# ===== CHAT =====
+def chat(user_input, history):
+    answer, source = get_answer(user_input)
+    history.append((user_input, answer + "\n\n📌 Source: " + source))
+    return "", history
+
+
+# ===== FEEDBACK =====
+def feedback(msg):
+    print("Feedback:", msg)
+    return "✅ Feedback received"
+
+
+# ===== UI =====
+with gr.Blocks(css="""
+.gradio-container {max-width: 100% !important;}
+""") as demo:
+
+    gr.Markdown("# 🤖 DocuMind")
+
+    file = gr.File(label="📄 Upload PDF")
+    status = gr.Textbox(label="Status")
+
+    process_btn = gr.Button("Process PDF")
+
+    chatbot = gr.Chatbot(height=400)
+
+    with gr.Row():
+        txt = gr.Textbox(placeholder="Ask question...")
+        send = gr.Button("Send")
+
+    clear = gr.Button("Clear Chat")
+
+    gr.Markdown("### 💬 Feedback")
+    fb = gr.Textbox(placeholder="Suggestions...")
+    fb_btn = gr.Button("Submit")
+    fb_out = gr.Textbox(label="Status")
+
+    process_btn.click(process_pdf, file, status)
+    send.click(chat, [txt, chatbot], [txt, chatbot])
+    txt.submit(chat, [txt, chatbot], [txt, chatbot])
+    clear.click(lambda: [], None, chatbot)
+    fb_btn.click(feedback, fb, fb_out)
+
+demo.launch()

requirements.txt

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+gradio
+rapidfuzz
+pymupdf
+easyocr
+numpy
+langchain
+langchain-community
+langchain-text-splitters
+faiss-cpu
+sentence-transformers