Create app.py

app.py

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+import gradio as gr
+import requests
+import subprocess
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# Globals
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+vector_index = None
+indexed_chunks = []
+url_cache = {}  # url -> (chunks, faiss index)
+
+FIRECRAWL_API_KEY = "sk_lBtoP3fxN5Z5z9D7WJAdGUIhR9uoWB6w0IIzqjRZ0Q8yq6Nc"  # Replace this
+
+# Ollama call helper
+def ollama_generate(system_prompt: str, user_prompt: str, model: str = "llama2") -> str:
+    """
+    Calls local Ollama LLM via CLI and returns generated text.
+    """
+    try:
+        command = [
+            "ollama",
+            "generate",
+            model,
+            "--system", system_prompt,
+            "--prompt", user_prompt,
+            "--quiet",
+            "--json"
+        ]
+        result = subprocess.run(command, capture_output=True, text=True, check=True)
+        # Ollama JSON output format: {"text":"generated text..."}
+        import json
+        output = json.loads(result.stdout)
+        return output.get("text", "").strip()
+    except Exception as e:
+        return f"Error calling Ollama: {str(e)}"
+
+# Scrape URL and embed content
+def scrape_and_embed(url: str):
+    global vector_index, indexed_chunks
+
+    if url in url_cache:
+        indexed_chunks, vector_index = url_cache[url]
+        return f"✅ Loaded cached content for {url}"
+
+    # Firecrawl scrape
+    response = requests.post(
+        "https://api.firecrawl.dev/v1/scrape",
+        headers={"Authorization": f"Bearer {FIRECRAWL_API_KEY}"},
+        json={"url": url, "javascript": False}
+    )
+    if response.status_code != 200:
+        return f"❌ Failed to scrape URL: {response.status_code}"
+
+    content = response.json().get("text", "")
+    chunks = [line.strip() for line in content.split("\n") if len(line.strip()) > 50]
+    indexed_chunks = chunks[:100]
+
+    # Embeddings + FAISS index
+    embeddings = embedding_model.encode(indexed_chunks)
+    vector_index = faiss.IndexFlatL2(embeddings.shape[1])
+    vector_index.add(np.array(embeddings))
+
+    # Cache it
+    url_cache[url] = (indexed_chunks, vector_index)
+
+    return f"✅ Scraped and indexed {len(indexed_chunks)} chunks from {url}"
+
+# Main RAG + Ollama Q&A function
+def web_rag_ollama(combined_input: str) -> str:
+    """
+    Expects input: "<URL> || <question>"
+    Scrapes URL (cached), embeds, retrieves context, then asks Ollama to answer.
+    """
+    global vector_index, indexed_chunks
+
+    if "||" not in combined_input:
+        return "❌ Input format must be: <URL> || <your question>"
+
+    url, question = [part.strip() for part in combined_input.split("||", 1)]
+
+    # Scrape and embed
+    scrape_status = scrape_and_embed(url)
+    if scrape_status.startswith("❌"):
+        return scrape_status
+
+    # Retrieval
+    if not indexed_chunks or vector_index is None:
+        return "⚠️ No indexed content available."
+
+    query_emb = embedding_model.encode([question])
+    D, I = vector_index.search(np.array(query_emb), k=3)
+    context = "\n\n".join([indexed_chunks[i] for i in I[0]])
+
+    # Ollama prompt engineering
+    system_prompt = (
+        "You are a helpful assistant. Use the provided context to answer the question. "
+        "If the answer is not contained in the context, say you don't know."
+    )
+    user_prompt = f"Context:\n{context}\n\nQuestion:\n{question}\n\nAnswer:"
+
+    # Call Ollama
+    answer = ollama_generate(system_prompt, user_prompt)
+
+    return f"**Scrape status:** {scrape_status}\n\n**Answer:**\n{answer}"
+
+# Gradio interface with MCP support
+demo = gr.Interface(
+    fn=web_rag_ollama,
+    inputs=gr.Textbox(
+        label="Input",
+        placeholder="Enter input in format:\nhttps://example.com || What is this page about?"
+    ),
+    outputs=gr.Textbox(label="Answer"),
+    title="🌐 Web RAG Q&A with Ollama (MCP-ready)",
+    description="Scrape URL, embed content, and answer questions using local Ollama LLM."
+)
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)