Create app.py

app.py

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+import os
+import gradio as gr
+import PyPDF2
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# -------- Step 1: Set and Verify Groq API Key --------
+os.environ["GROQ_API_KEY"] = "your-real-groq-api-key"  # Replace this
+api_key = os.getenv("GROQ_API_KEY")
+if not api_key:
+    raise ValueError("❌ GROQ_API_KEY not found.")
+
+client = Groq(api_key=api_key)
+
+# -------- Step 2: Setup Model --------
+model = SentenceTransformer("all-MiniLM-L6-v2")
+faiss_index = None
+chunks_list = []
+
+# -------- Step 3: Extract Text from PDF --------
+def extract_text_from_pdf(pdf_path, logs):
+    try:
+        text = ""
+        logs += "📖 Step 1: Reading PDF...\n"
+        with open(pdf_path, 'rb') as file:
+            reader = PyPDF2.PdfReader(file)
+            for page in reader.pages:
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text
+        if not text.strip():
+            raise ValueError("❌ No readable text found in PDF.")
+        logs += "✅ Text extracted successfully.\n"
+        return text, logs
+    except Exception as e:
+        logs += f"❌ Error during PDF text extraction: {str(e)}\n"
+        return None, logs
+
+# -------- Step 4: Chunking --------
+def create_chunks(text, chunk_size, logs):
+    try:
+        logs += "🧩 Step 2: Creating text chunks...\n"
+        words = text.split()
+        chunks = [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
+        logs += f"✅ {len(chunks)} chunks created.\n"
+        return chunks, logs
+    except Exception as e:
+        logs += f"❌ Error in chunking: {str(e)}\n"
+        return None, logs
+
+# -------- Step 5: Embeddings & Indexing --------
+def embed_chunks(chunks, logs):
+    try:
+        logs += "📊 Step 3: Generating embeddings and creating FAISS index...\n"
+        embeddings = model.encode(chunks)
+        index = faiss.IndexFlatL2(embeddings.shape[1])
+        index.add(np.array(embeddings))
+        logs += "✅ Embeddings & index created successfully.\n"
+        return index, chunks, logs
+    except Exception as e:
+        logs += f"❌ Error in embedding/indexing: {str(e)}\n"
+        return None, None, logs
+
+# -------- Step 6: Process PDF --------
+def process_pdf(file, chunk_size=200):
+    global faiss_index, chunks_list
+    logs = "📂 File uploaded successfully.\n"
+    try:
+        text, logs = extract_text_from_pdf(file.name, logs)
+        if not text:
+            return logs
+
+        chunks, logs = create_chunks(text, chunk_size, logs)
+        if not chunks:
+            return logs
+
+        index, chunks, logs = embed_chunks(chunks, logs)
+        if not index:
+            return logs
+
+        faiss_index = index
+        chunks_list = chunks
+        logs += "🎉 Step 4: PDF processed and ready for Q&A.\n"
+        return logs
+    except Exception as e:
+        logs += f"❌ Error during processing: {str(e)}\n"
+        return logs
+
+# -------- Step 7: Ask Question --------
+def answer_question(query):
+    logs = "🤖 Step 5: Processing your question...\n"
+    try:
+        if faiss_index is None:
+            return "❌ Please process a PDF first."
+
+        query_embedding = model.encode([query])
+        _, I = faiss_index.search(np.array(query_embedding), k=3)
+        relevant_chunks = [chunks_list[i] for i in I[0] if i < len(chunks_list)]
+
+        if not relevant_chunks:
+            return "❌ No relevant content found to answer your question."
+
+        context = "\n".join(relevant_chunks)
+        prompt = f"Answer the question based on the context below:\n\nContext:\n{context}\n\nQuestion: {query}"
+
+        logs += "🧠 Sending to Groq LLaMA3 model...\n"
+        response = client.chat.completions.create(
+            model="llama3-70b-8192",
+            messages=[{"role": "user", "content": prompt}]
+        )
+
+        answer = response.choices[0].message.content
+        logs += "✅ Answer generated successfully.\n"
+        return f"{answer}\n\n{logs}"
+
+    except Exception as e:
+        logs += f"❌ Error during answering: {str(e)}\n"
+        return logs
+
+# -------- Step 8: Gradio UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 RAG PDF Q&A App with Groq + FAISS (Debug-Friendly)")
+
+    with gr.Row():
+        pdf_input = gr.File(label="📂 Upload PDF", type="filepath")
+        process_btn = gr.Button("⚙️ Process PDF")
+        log_output = gr.Textbox(label="📝 Logs", lines=20)
+
+    with gr.Row():
+        question_input = gr.Textbox(label="❓ Ask a Question")
+        answer_btn = gr.Button("💬 Get Answer")
+        answer_output = gr.Textbox(label="📜 Answer + Logs", lines=20)
+
+    process_btn.click(fn=process_pdf, inputs=pdf_input, outputs=log_output)
+    answer_btn.click(fn=answer_question, inputs=question_input, outputs=answer_output)
+
+demo.launch()