Create app.py

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import os
+from huggingface_hub import InferenceClient
+from sentence_transformers import SentenceTransformer
+import faiss
+
+# Load text from stored data
+with open("verilog.txt", "r", encoding="utf-8") as f:
+    data = f.read()
+
+# Simple chunking
+chunks = data.split("\n\n")
+
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+embeddings = embedder.encode(chunks)
+
+# Create Faiss index
+dimension = embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(embeddings)
+
+def rag_retrieve(query, top_k=3):
+    query_emb = embedder.encode([query])
+    distances, indices = index.search(query_emb, top_k)
+    retrieved_chunks = [chunks[i] for i in indices[0]]
+    return "\n".join(retrieved_chunks)
+
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+
+    retrieved_context = rag_retrieve(message)
+
+    rag_augmented_system = (
+        f"{system_message}\n\n"
+        "Relevant medical guidance below:\n"
+        f"{retrieved_context}\n\n"
+        "Use this information while responding clearly and politely."
+    )
+
+    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+
+    messages = [{"role": "system", "content": rag_augmented_system}]
+    messages.extend(history)
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        choices = message.choices
+        token = ""
+        if len(choices) and choices[0].delta.content:
+            token = choices[0].delta.content
+
+        response += token
+        yield response
+
+
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    additional_inputs=[
+        gr.Textbox(value="You are an AI based knowledge base of the ICT project of 16-bit RISC processor built in verilog by Hashir Ehtisham, Abdullah Ikram and Hadi Khan Lodhi.", label="System message"),
+        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+with gr.Blocks() as demo:
+    with gr.Sidebar():
+        gr.LoginButton()
+    chatbot.render()
+
+
+if __name__ == "__main__":
+    demo.launch()