Update app.py

app.py

@@ -1,64 +1,113 @@
+import os
+from typing import List, Optional
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    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,
-    ):
-        token = message.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
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import TextLoader
+from langchain.docstore.document import Document
+from langchain.chains import RetrievalQA
+from langchain.llms.base import LLM
+from groq import Groq
+import pypdf  # PyMuPDF
+
+
+# --- Custom LLM class using Groq ---
+class GroqLLM(LLM):
+    model: str = "llama3-8b-8192"
+    api_key: str = "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"  # <-- PUT YOUR GROQ API KEY HERE
+    temperature: float = 0.0
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        client = Groq(api_key=self.api_key)
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+        ]
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=self.temperature,
+        )
+        return response.choices[0].message.content
+
+    @property
+    def _llm_type(self) -> str:
+        return "groq-llm"
+
+
+# --- RAG Setup ---
+retriever = None
+qa_chain = None
+
+
+def extract_text_from_pdf(file_path: str) -> str:
+    doc = fitz.open(file_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    doc.close()
+    return text
+
+
+def process_file(file_obj):
+    global retriever, qa_chain
+
+    ext = os.path.splitext(file_obj.name)[1].lower()
+    try:
+        # Load content
+        if ext == ".pdf":
+            text = extract_text_from_pdf(file_obj.name)
+        elif ext == ".txt":
+            with open(file_obj.name, "r", encoding="utf-8") as f:
+                text = f.read()
+        else:
+            return "❌ Unsupported file format. Please upload a .txt or .pdf file."
+
+        # Create document chunks
+        document = Document(page_content=text)
+        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+        docs = splitter.split_documents([document])
+
+        # Vectorstore with HuggingFace embeddings
+        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        vectorstore = Chroma.from_documents(docs, embedding=embeddings, persist_directory="rag_store")
+
+        retriever = vectorstore.as_retriever()
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=GroqLLM(),
+            retriever=retriever,
+            return_source_documents=True
+        )
+
+        return "✅ File processed successfully. You can now ask questions."
+
+    except Exception as e:
+        return f"❌ Error processing file: {e}"
+
+
+def ask_question(query):
+    if qa_chain is None:
+        return "⚠ Please upload a file first."
+    result = qa_chain({"query": query})
+    return result["result"]
+
+
+# --- Gradio UI ---
+with gr.Blocks(title="RAG PDF & Text Chatbot") as demo:
+    gr.Markdown("## 🧠 RAG-powered Q&A Chatbot (Groq + LangChain)")
+    gr.Markdown("Upload a .pdf or .txt file and ask questions based on its content.")
+
+    file_input = gr.File(label="Upload PDF or Text File", file_types=[".pdf", ".txt"])
+    upload_status = gr.Textbox(label="Status", interactive=False)
+
+    file_input.change(fn=process_file, inputs=file_input, outputs=upload_status)
+
+    question_box = gr.Textbox(label="Ask your question")
+    answer_box = gr.Textbox(label="Answer", interactive=False)
+
+    submit_btn = gr.Button("Get Answer")
+    submit_btn.click(fn=ask_question, inputs=question_box, outputs=answer_box)
+
+demo.launch()