Added API Key (#1)

- Added API Key (8d382e3decedde1a4d8c33346a9910c78b67a164)

app.py

@@ -1,70 +1,157 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    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
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, 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()
+import os
+import tempfile
+from typing import List
+
+import PyPDF2
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+from groq import Groq
+
+# -----------------------------
+# Configuration
+# -----------------------------
+GROQ_API_KEY = os.getenv("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")
+MODEL_NAME = "llama3-8b-8192"
+
+client = Groq(api_key=GROQ_API_KEY)
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# -----------------------------
+# PDF Processing
+# -----------------------------
+def extract_text_from_pdfs(files: List[tempfile.NamedTemporaryFile]):
+    documents = []
+    for file in files:
+        reader = PyPDF2.PdfReader(file)
+        for page_num, page in enumerate(reader.pages):
+            text = page.extract_text()
+            if text:
+                documents.append({
+                    "text": text,
+                    "source": f"{os.path.basename(file.name)} - page {page_num + 1}"
+                })
+    return documents
+
+# -----------------------------
+# Chunking
+# -----------------------------
+def chunk_text(documents, chunk_size=500, overlap=50):
+    chunks = []
+    for doc in documents:
+        text = doc["text"]
+        words = text.split()
+        start = 0
+        while start < len(words):
+            chunk_words = words[start:start + chunk_size]
+            chunk_text = " ".join(chunk_words)
+            chunks.append({
+                "text": chunk_text,
+                "source": doc["source"]
+            })
+            start += chunk_size - overlap
+    return chunks
+
+# -----------------------------
+# Embeddings & Retrieval
+# -----------------------------
+def embed_chunks(chunks):
+    texts = [c["text"] for c in chunks]
+    embeddings = embedder.encode(texts)
+    return embeddings
+
+def retrieve_relevant_chunks(query, chunks, embeddings, top_k=3):
+    query_embedding = embedder.encode([query])
+    similarities = cosine_similarity(query_embedding, embeddings)[0]
+    top_indices = np.argsort(similarities)[-top_k:][::-1]
+
+    results = []
+    for idx in top_indices:
+        results.append(chunks[idx])
+    return results
+
+# -----------------------------
+# LLM Call
+# -----------------------------
+def ask_llm(question, context, history):
+    messages = history.copy()
+
+    system_prompt = (
+        "You are a helpful assistant. Answer the question strictly using the provided context. "
+        "If the answer is not in the context, say so."
+    )
+
+    messages.insert(0, {"role": "system", "content": system_prompt})
+    messages.append({
+        "role": "user",
+        "content": f"Context:\n{context}\n\nQuestion:\n{question}"
+    })
+
+    response = client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        temperature=0.3
+    )
+
+    return response.choices[0].message.content
+
+# -----------------------------
+# Main Chat Logic
+# -----------------------------
+def chat(files, question, chat_history):
+    if not files:
+        return chat_history, "Please upload PDF files first."
+
+    documents = extract_text_from_pdfs(files)
+    chunks = chunk_text(documents)
+    embeddings = embed_chunks(chunks)
+
+    relevant_chunks = retrieve_relevant_chunks(question, chunks, embeddings)
+
+    context = ""
+    sources = []
+    for c in relevant_chunks:
+        context += c["text"] + "\n\n"
+        sources.append(c["source"])
+
+    answer = ask_llm(question, context, chat_history)
+
+    answer_with_sources = (
+        f"{answer}\n\n"
+        f"Sources:\n" + "\n".join(set(sources))
+    )
+
+    chat_history.append({"role": "user", "content": question})
+    chat_history.append({"role": "assistant", "content": answer_with_sources})
+
+    return chat_history, answer_with_sources
+
+# -----------------------------
+# Gradio UI
+# -----------------------------
+with gr.Blocks(title="Enhanced RAG Chatbot") as demo:
+    gr.Markdown("## 📚 Enhanced RAG-Based Chatbot (PDF QA)")
+    gr.Markdown("Upload multiple PDFs and ask questions based on their content.")
+
+    with gr.Row():
+        pdf_files = gr.File(
+            label="Upload PDF Files",
+            file_types=[".pdf"],
+            file_count="multiple"
+        )
+
+    chatbot = gr.Chatbot()
+    question = gr.Textbox(label="Ask a question")
+    state = gr.State([])
+
+    submit = gr.Button("Ask")
+
+    submit.click(
+        fn=chat,
+        inputs=[pdf_files, question, state],
+        outputs=[chatbot, chatbot]
+    )
+
+demo.launch()