Update app.py

app.py

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+import os
+import gradio as gr
+from pypdf import PdfReader
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import requests
+
+# Set your Groq API key and model
+GROQ_API_KEY = os.getenv("GROQ_API_KEY", "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")
+GROQ_MODEL = "llama3-8b-8192"
+
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+def extract_text_from_pdf(file):
+    reader = PdfReader(file)
+    return "\n".join(page.extract_text() or "" for page in reader.pages)
+
+def embed_document(text, chunk_size=500):
+    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+    embeddings = embedding_model.encode(chunks)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings))
+    return chunks, index
+
+def query_groq(prompt):
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": GROQ_MODEL,
+        "messages": [
+            {
+                "role": "system",
+                "content": (
+                    "You are a helpful and knowledgeable AI assistant. A user has uploaded a document. "
+                    "Your task is to analyze the content of the document and provide accurate, clear, and concise answers to any questions "
+                    "the user asks based on that document. If the answer is not found in the document, politely state that the information is not available in the provided file."
+                )
+            },
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.3
+    }
+
+    response = requests.post(url, headers=headers, json=payload)
+    try:
+        data = response.json()
+        if 'choices' in data:
+            return data['choices'][0]['message']['content']
+        elif 'error' in data:
+            return f"❌ API Error: {data['error']['message']}"
+        else:
+            return "❌ Unexpected API response:\n" + str(data)
+    except Exception as e:
+        return f"❌ Failed to parse response: {e}\nRaw: {response.text}"
+
+doc_chunks = []
+doc_index = None
+
+def handle_upload(file):
+    global doc_chunks, doc_index
+    text = extract_text_from_pdf(file.name)
+    doc_chunks, doc_index = embed_document(text)
+    return "✅ Document processed. You may now ask questions."
+
+def answer_question(question):
+    if not doc_chunks or doc_index is None:
+        return "⚠️ Please upload a document first."
+
+    query_embedding = embedding_model.encode([question])
+    D, I = doc_index.search(np.array(query_embedding), k=5)
+    context = "\n\n".join([doc_chunks[i] for i in I[0]])
+    prompt = f"The user asked: '{question}'\n\nUse the following document content to answer:\n{context}"
+    return query_groq(prompt)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 RAG App with Groq API (PDF-Based Q&A)")
+    with gr.Row():
+        file_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+        upload_btn = gr.Button("Process Document")
+    upload_status = gr.Textbox(label="Status", interactive=False)
+
+    question = gr.Textbox(label="Ask a question about the document")
+    answer = gr.Textbox(label="Answer", lines=5)
+
+    upload_btn.click(fn=handle_upload, inputs=file_input, outputs=upload_status)
+    question.submit(fn=answer_question, inputs=question, outputs=answer)
+
+demo.launch()