Update app.py

app.py

@@ -8,8 +8,21 @@ from langchain.document_loaders import PyPDFLoader, UnstructuredFileLoader, CSVL
 from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 
-# Initialize the Zephyr client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# # Initialize the Zephyr client
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+from huggingface_hub import InferenceClient
+
+# Access the Hugging Face token from environment variables
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+
+if not HF_API_TOKEN:
+    raise ValueError("Hugging Face API token is not set in environment variables.")
+
+# Initialize the client with the token
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_API_TOKEN)
+
+
 
 # Function to load documents based on file type
 def load_documents(file_path):
@@ -93,19 +106,36 @@ def handle_query(message, history, system_message, max_tokens, temperature, top_
     return respond(message, history, system_message, max_tokens, temperature, top_p, retriever)
 
 # Gradio app setup
-demo = gr.ChatInterface(
-    fn=handle_query,
-    additional_inputs=[
-        gr.File(label="Upload File", type="file"),
+# demo = gr.ChatInterface(
+#     fn=handle_query,
+#     additional_inputs=[
+#         gr.File(label="Upload File", type="file"),
+#         gr.Textbox(value="You are a knowledgeable assistant.", label="System Message"),
+#         gr.Slider(1, 2048, step=1, value=512, label="Max Tokens"),
+#         gr.Slider(0.1, 4.0, step=0.1, value=0.7, label="Temperature"),
+#         gr.Slider(0.1, 1.0, step=0.05, value=0.95, label="Top-p"),
+#     ],
+#     outputs="text",
+#     title="RAG with Zephyr-7B",
+#     description="A Retrieval-Augmented Generation chatbot powered by Zephyr-7B and Chroma vector database.",
+# )
+
+demo = gr.Interface(
+    fn=handle_uploaded_file,  # Handle uploaded files
+    inputs=[
+        gr.File(label="Upload Document"),
         gr.Textbox(value="You are a knowledgeable assistant.", label="System Message"),
+        gr.Textbox(label="Enter Your Query", placeholder="Ask a question..."),
         gr.Slider(1, 2048, step=1, value=512, label="Max Tokens"),
         gr.Slider(0.1, 4.0, step=0.1, value=0.7, label="Temperature"),
         gr.Slider(0.1, 1.0, step=0.05, value=0.95, label="Top-p"),
     ],
     outputs="text",
     title="RAG with Zephyr-7B",
-    description="A Retrieval-Augmented Generation chatbot powered by Zephyr-7B and Chroma vector database.",
+    description="Upload documents and ask questions using RAG.",
 )
 
+
+
 if __name__ == "__main__":
     demo.launch()