Update app.py

app.py

@@ -1,25 +1,86 @@
 import gradio as gr
 import importlib
 
-def run_selected_app(app_file):
-    try:
-        module = importlib.import_module(app_file)  # Import the specified module
-        interface = getattr(module, "iface")  # Get the Gradio interface from the module
-        interface.launch(share=True)  # Launch the selected app in a new tab
-    except Exception as e:
-        return f"Error loading app: {e}"
+import re
+import gradio as gr
+import os
+import google.generativeai as genai
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+
+import chromadb
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from uuid import uuid4
+import gradio as gr
+
+# Now you can use hugging_face_api_key in your code
+
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel('gemini-pro')  # Load the model
+
+def get_Answer(query):
+    res = collection.query(  # Assuming `collection` is defined elsewhere
+        query_texts=query,
+        n_results=2
+    )
+    system = f"""You are a teacher. You will be provided some context, 
+    your task is to analyze the relevant context and answer the below question:
+    - {query}
+    """
+    context = " ".join([re.sub(r'[^\x00-\x7F]+', ' ', r) for r in res['documents'][0]])
+    prompt = f"### System: {system} \n\n ###: User: {context} \n\n ### Assistant:\n"
+    answer = model.generate_content(prompt).text
+    return answer
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=get_Answer,
+    inputs=gr.Textbox(lines=5, placeholder="Ask a question"),  # Textbox for query
+    outputs="textbox",  # Display the generated answer in a textbox
+    title="Answer Questions with Gemini-Pro",
+    description="Ask a question and get an answer based on context from a ChromaDB collection.",
+)
+
+
 
-    return "Selected app launched successfully!"
 
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=800,
+    chunk_overlap=50
+)
+client = chromadb.PersistentClient("test")
+collection = client.create_collection("test_data")
+
+def upload_pdf(file_path):
+    loader = PyPDFLoader(file_path)
+    pages = loader.load()
+    documents = []
+    for page in pages:
+        docs = text_splitter.split_text(page.page_content)
+        for doc in docs:
+            documents.append({
+                "text": docs, "meta_data": page.metadata,
+            })
+    collection.add(
+        ids=[str(uuid4()) for _ in range(len(documents))],
+        documents=[doc['text'][0] for doc in documents],
+        metadatas=[doc['meta_data'] for doc in documents]
+    )
+    return f"PDF Uploaded Successfully. {collection.count()} chunks stored in ChromaDB"
+
+# Define the Gradio interface
 iface = gr.Interface(
-    fn=run_selected_app,
-    inputs=gr.Dropdown(["upload_pdf.py", "query.py"], label="Select App"),
-    outputs="textbox",
-    title="Run Gradio Apps",
-    description="Choose an app to run from the dropdown."
+    fn=upload_pdf,
+    inputs=["file"],  # Specify a file input component
+    outputs="textbox",  # Display the output text in a textbox
+    title="Upload PDF to ChromaDB",
+    description="Upload a PDF file and store its text chunks in ChromaDB.",
 )
 
-iface.launch()
+
+# Launch the Gradio app
+iface.launch(debug=True,share=True)