Create app.py

app.py

@@ -0,0 +1,71 @@
+import json
+from pathlib import Path
+import gradio as gr
+import os
+import shutil
+import json
+import openai
+
+api_key = os.getenv("OPENAI-API-KEY")
+
+# Load the OpenAI API key
+openai.api_key = api_key
+
+def ask_chatbot(query, chat_history, project_data):
+    # Convert project_data to a string format (optional: simplify or summarize if needed)
+    project_data_str = json.dumps(project_data, indent=2)
+
+    if len(chat_history) >= 5:
+        chat_history.pop(0)
+
+    query_with_history = ""
+    for question, answer in chat_history:
+        query_with_history += f"\nUser: {question}\nAssistant: {answer}"
+
+    query_with_history += f"\nUser: {query}"
+
+
+    # Define the messages
+    messages = [
+        {"role": "system", "content": "You are a virtual project management assistant. Analyzing the given project information for a website redesign project, you have to answer project managers' questions:"},
+        {"role": "system", "content": project_data_str},
+        {"role": "user", "content": query_with_history}
+    ]
+
+    # Call the OpenAI API
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=messages,
+        max_tokens=512
+    )
+
+    # Extract the response text
+    answer = response.choices[0].message['content'].strip()
+
+    return answer
+
+
+def generate_response(message, history):
+  project_data = {}
+  if os.path.exists("/content/project_data.json"):
+    with open("/content/project_data.json", 'r') as f:
+      project_data = json.load(f)
+  return ask_chatbot(message, history, project_data)
+
+
+
+def upload_file(data_file):
+  with open(data_file.name, "r") as f:
+    data = json.load(f)
+  gr.Info("Prooject file Uploaded. You can now query the document")
+  with open("/content/project_data.json", "w") as f:
+    json.dump(data, f)
+
+with gr.Blocks() as demo:
+  with gr.Column():
+    u = gr.UploadButton("Upload a file", file_count="single")
+    u.upload(upload_file, u)
+  gr.ChatInterface(generate_response)
+
+demo.launch()
+