Upload folder using huggingface_hub

.gitignore

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+.env
+simulated_data/
+__pycache__/
+*.pyc
+flagged/

README.md

@@ -1,12 +1,6 @@
 ---
-title: MentorMatcher Demo Public
-emoji: 🏆
-colorFrom: blue
-colorTo: pink
+title: MentorMatcher-Demo-Public
+app_file: app.py
 sdk: gradio
 sdk_version: 4.44.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+import os
+import gradio as gr
+from dotenv import load_dotenv
+import asyncio
+import traceback
+# ... (keep other imports)
+
+# Load .env file
+load_dotenv()
+
+# ... (keep other imports and function definitions)
+
+# Add this new function to load and use the Hugging Face space
+def load_hf_space():
+    HF_TOKEN = os.environ.get("HF_TOKEN")
+    try:
+        interface = gr.Interface.load(
+            name="jjia11/MentorMatch-Demo",
+            src="huggingface",
+            hf_token=HF_TOKEN,
+        )
+        print("Hugging Face space loaded successfully")
+        return interface
+    except Exception as e:
+        print(f"Error loading Hugging Face space: {str(e)}")
+        return None
+
+# Load the Hugging Face space
+hf_space = load_hf_space()
+
+# Modify the process_cv_wrapper function to use the Hugging Face space
+def process_cv_wrapper(file, num_candidates):
+    async def async_wrapper():
+        try:
+            if hf_space is None:
+                raise ValueError("Hugging Face space is not loaded")
+
+            # Use the Hugging Face space to process the CV
+            result = hf_space.predict(
+                file.name,  # Assuming the space expects a file path
+                num_candidates,
+                api_name="/process_cv"  # Replace with your actual API endpoint
+            )
+
+            # Assuming the result contains the expected outputs
+            mentee_summary, mentor_table_html, evaluated_matches, csv_data = result
+
+            return mentee_summary, mentor_table_html, evaluated_matches, csv_data
+        except Exception as e:
+            print(f"An error occurred: {str(e)}")
+            print("Traceback:")
+            print(traceback.format_exc())
+            return "Error occurred", "Error occurred", [], []
+
+    return asyncio.run(async_wrapper())
+
+# Modify the chat_query function to use the Hugging Face space
+async def chat_query(message, history, index_choice):
+    try:
+        if hf_space is None:
+            raise ValueError("Hugging Face space is not loaded")
+
+        # Use the Hugging Face space for chat functionality
+        response = hf_space.predict(
+            message,
+            history,
+            index_choice,
+            api_name="/chat"  # Replace with your actual chat API endpoint
+        )
+
+        # Assuming the response is already in the correct format
+        return response, ""
+    except Exception as e:
+        print(f"An error occurred in chat: {str(e)}")
+        return history + [[message, "An error occurred. Please try again."]], ""
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.HTML("<h1>TCH Mentor-Mentee Matching System</h1>")
+    
+    with gr.Tab("Mentor Search"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                file = gr.File(label="Upload Mentee CV (PDF)")
+
+            with gr.Column(scale=1):
+                num_candidates = gr.Number(label="Number of Candidates", value=5, minimum=1, maximum=100, step=1)
+                submit_btn = gr.Button("Submit")
+
+        summary = gr.Textbox(label="Student CV Summary")
+        mentor_table = gr.HTML(label="Matching Mentors Table", value="<div style='height: 500px;'>Results will appear here after submission.</div>")
+        download_btn = gr.Button("Download Results as CSV")
+
+        evaluated_matches = gr.State([])
+        csv_data = gr.State([])
+
+        submit_btn.click(
+            fn=process_cv_wrapper,
+            inputs=[file, num_candidates],
+            outputs=[summary, mentor_table, evaluated_matches, csv_data],
+            show_progress=True
+        )
+
+        download_btn.click(
+            fn=download_csv,
+            inputs=[csv_data],
+            outputs=gr.File(label="Download CSV", height=30),
+            show_progress=False,
+        )
+
+    with gr.Tab("Chat"):
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label="Type your message here...")
+        clear = gr.Button("Clear Chat")
+
+        chat_index_choice = gr.Dropdown(
+            choices=["Assistant Professors and Above", "Above Assistant Professors"],
+            label="Select Index for Chat",
+            value="Assistant Professors and Above"
+        )
+
+        msg.submit(chat_query, inputs=[msg, chatbot, chat_index_choice], outputs=[chatbot, msg])
+        clear.click(lambda: ([], ""), outputs=[chatbot, msg])
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(share=True)

requirements.txt

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+PyPDF2==3.0.1
+tqdm==4.66.4
+python-dotenv==1.0.1
+gradio==4.36.1
+gradio_client==1.0.1
+pandas==2.2.2
+gpt4all==2.7.0
+langchain-openai>=0.1.21
+faiss-cpu>=1.8.0
+tenacity==8.4.1
+pypdf==4.2.0
+openai
+langchain-community
+langchain-core
+asyncio
+aiohttp
+python-docx