Update app.py

app.py

@@ -1,63 +1,62 @@
+import openai
 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)",
-        ),
+import os
+import PyPDF2
+
+# Load your OpenAI API key from the environment variable
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Function to extract text from an uploaded PDF
+def extract_text_from_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to interact with the GPT-4o-mini model via the OpenAI API
+def subsurface_sentinel(user_input, pdf_file=None):
+    context = ""
+    
+    if pdf_file is not None:
+        context = extract_text_from_pdf(pdf_file)
+        # Truncate or summarize context if needed, depending on the length
+        context = context[:2000]  # Truncate to first 2000 characters for example
+    
+    # Combine the context with the user input
+    prompt = f"Context: {context}\n\nUser: {user_input}"
+    
+    response = openai.chat.completions.create(  # Correct method usage
+        model="gpt-4o-mini",  # Use the gpt-4o-mini model
+        messages=[
+            {"role": "system", "content": "You are Subsurface Sentinel, a virtual assistant for professionals in the oil and gas industry, focusing on field development planning."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=150,
+        temperature=0.7,
+    )
+    
+    # Extract the response text and remove any leading/trailing whitespace
+    return response.choices[0].message.content.strip()
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=subsurface_sentinel,  # The function to be called for generating responses
+    inputs=[
+        gr.Textbox(label="Enter your question here"),  # User input
+        gr.File(label="Upload a PDF for context (optional)")  # PDF upload input
     ],
+    outputs="text",  # Output type for the bot's response
+    title="Subsurface Sentinel",
+    description=(
+        "A virtual assistant for professionals in the oil and gas industry focusing on field development planning.\n\n"
+        "You can ask a question or upload a PDF to provide additional context for your query."
+    ),
+    examples=[
+        ["Analyze the provided Field Development Plan for insights.", None], 
+        ["What are the potential risks in subsurface operations?", None]
+    ]
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+iface.launch()