Delete app.py

app.py

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-import os
-import gradio as gr
-from gradio import ChatMessage
-from typing import Iterator
-import google.generativeai as genai
-
-# get Gemini API Key from the environ variable
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=GEMINI_API_KEY)
-
-# we will be using the Gemini 2.0 Flash model with Thinking capabilities
-model = genai.GenerativeModel("gemini-2.0-flash-thinking-exp-1219") # Consider Gemini Pro Vision for Image input
-
-def format_chat_history(messages: list) -> list:
-    """
-    Formats the chat history into a structure Gemini can understand
-    """
-    formatted_history = []
-    for message in messages:
-        # Skip thinking messages (messages with metadata)
-        if not (message.get("role") == "assistant" and "metadata" in message):
-            formatted_history.append({
-                "role": "user" if message.get("role") == "user" else "assistant",
-                "parts": [message.get("content", "")]
-            })
-    return formatted_history
-
-def stream_gemini_response(message_input: str|gr.File, messages: list) -> Iterator[list]:
-    """
-    Streams thoughts and response with conversation history support, handling text or file input.
-    """
-    user_message = ""
-    input_file = None
-
-    if isinstance(message_input, str):
-        user_message = message_input
-        print(f"\n=== New Request (Text) ===")
-        print(f"User message (raw): {repr(user_message)}") # Debug print raw value
-        print(f"User message: {user_message}")
-        if not user_message: # Check if text message is explicitly empty (empty string "" directly)
-            messages.append(ChatMessage(role="assistant", content="Please provide a non-empty text message or upload a file. Empty text input is not allowed.")) # More specific message
-            yield messages
-            return
-
-    elif isinstance(message_input, gr.File): #gr.File directly should be used with newer gradio versions (v4+)
-        input_file = message_input.name # Access the temporary file path
-        file_type = message_input.original_name.split('.')[-1].lower() #Get original filename's extension
-        print(f"\n=== New Request (File) ===")
-        print(f"File uploaded: {input_file}, type: {file_type}")
-
-        try:
-            with open(input_file, "rb") as f: #Open file in binary mode for universal handling
-                file_data = f.read()
-
-            if file_type in ['png', 'jpg', 'jpeg', 'gif']: #Example Image Types - expand as needed
-                user_message = {"inline_data": {"mime_type": f"image/{file_type}", "data": file_data}} #Prepare image part for Gemini
-            elif file_type == 'csv':
-                user_message = {"inline_data": {"mime_type": "text/csv", "data": file_data}} #Prepare csv part
-
-        except Exception as e:
-            print(f"Error reading file: {e}")
-            messages.append(ChatMessage(role="assistant", content=f"Error reading file: {e}"))
-            yield messages
-            return
-    else:
-        messages.append(ChatMessage(role="assistant", content="Sorry, I cannot understand this input format. Please use text or upload a valid file.")) # More informative error
-        yield messages
-        return
-
-
-    try:
-        # Format chat history for Gemini
-        chat_history = format_chat_history(messages)
-
-        # Initialize Gemini chat
-        chat = model.start_chat(history=chat_history)
-        response = chat.send_message(user_message, stream=True) #Send the message part as is
-
-        # Initialize buffers and flags - same as before
-        thought_buffer = ""
-        response_buffer = ""
-        thinking_complete = False
-
-        # Add initial thinking message - same as before
-        messages.append(
-            ChatMessage(
-                role="assistant",
-                content="",
-                metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-            )
-        )
-
-        for chunk in response: #streaming logic - same as before
-            parts = chunk.candidates[0].content.parts
-            current_chunk = parts[0].text
-
-            if len(parts) == 2 and not thinking_complete:
-                # Complete thought and start response
-                thought_buffer += current_chunk
-                print(f"\n=== Complete Thought ===\n{thought_buffer}")
-
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=thought_buffer,
-                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-                )
-                yield messages
-
-                # Start response
-                response_buffer = parts[1].text
-                print(f"\n=== Starting Response ===\n{response_buffer}")
-
-                messages.append(
-                    ChatMessage(
-                        role="assistant",
-                        content=response_buffer
-                    )
-                )
-                thinking_complete = True
-
-            elif thinking_complete:
-                # Stream response
-                response_buffer += current_chunk
-                print(f"\n=== Response Chunk ===\n{current_chunk}")
-
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=response_buffer
-                )
-
-            else:
-                # Stream thinking
-                thought_buffer += current_chunk
-                print(f"\n=== Thinking Chunk ===\n{thought_buffer}")
-
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=thought_buffer,
-                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-                )
-
-            yield messages
-
-        print(f"\n=== Final Response ===\n{response_buffer}")
-
-
-    except Exception as e:
-        print(f"\n=== Error ===\n{str(e)}")
-        messages.append(
-            ChatMessage(
-                role="assistant",
-                content=f"I apologize, but I encountered an error: {str(e)}"
-            )
-        )
-        yield messages
-
-def user_message(message_text, file_upload, history: list) -> tuple[str, None, list]:
-    """Adds user message to chat history"""
-    print(f"\n=== User Message Input Check ====") #debug
-    print(f"Message Text: {repr(message_text)}") #debug raw text value
-    print(f"File Upload: {file_upload}") #debug file upload object
-    msg = message_text if message_text else file_upload
-    history.append(ChatMessage(role="user", content=msg if isinstance(msg, str) else msg.name)) #Store message or filename in history.
-    return "", None, history #clear both input fields
-
-
-# Create the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="slate", neutral_hue="neutral")) as demo:
-    gr.Markdown("# Gemini 2.0 Flash 'Thinking' Chatbot 💭")
-
-    chatbot = gr.Chatbot(
-        type="messages",
-        label="Gemini2.0 'Thinking' Chatbot",
-        render_markdown=True,
-        scale=1,
-        avatar_images=(None,"https://lh3.googleusercontent.com/oxz0sUBF0iYoN4VvhqWTmux-cxfD1rxuYkuFEfm1SFaseXEsjjE4Je_C_V3UQPuJ87sImQK3HfQ3RXiaRnQetjaZbjJJUkiPL5jFJ1WRl5FKJZYibUA=w214-h214-n-nu")
-    )
-
-    with gr.Row(equal_height=True):
-        input_box = gr.Textbox(
-            lines=1,
-            label="Chat Message",
-            placeholder="Type your message here...",
-            scale=3
-        )
-        file_upload = gr.File(label="Upload File", file_types=["image", ".csv"], scale=2) # Allow image and CSV files
-
-        clear_button = gr.Button("Clear Chat", scale=1)
-
-    # Set up event handlers
-    msg_store = gr.State("")  # Store for preserving user message
-
-
-    input_box.submit(
-        user_message,
-        inputs=[input_box, file_upload, chatbot],
-        outputs=[input_box, file_upload, chatbot],
-        queue=False
-    ).then(
-        stream_gemini_response,
-        inputs=[input_box, chatbot], # Input either from text box or file, logic inside stream_gemini_response
-        outputs=chatbot
-    )
-
-    file_upload.upload(
-        user_message,
-        inputs=[input_box, file_upload, chatbot], # even textbox is input here so clearing both will work
-        outputs=[input_box, file_upload, chatbot],
-        queue=False
-    ).then(
-        stream_gemini_response,
-        inputs=[file_upload, chatbot], # Input is now the uploaded file.
-        outputs=chatbot
-    )
-
-
-    clear_button.click(
-        lambda: ([], "", ""),
-        outputs=[chatbot, input_box, msg_store],
-        queue=False
-    )
-
-    gr.Markdown(  # Description moved to the bottom
-        """
-        <br><br><br>  <!-- Add some vertical space -->
-        ---
-        ### About this Chatbot
-        This chatbot demonstrates the experimental 'thinking' capability of the **Gemini 2.0 Flash** model.
-        You can observe the model's thought process as it generates responses, displayed with the "⚙️ Thinking" prefix.
-        **Key Features:**
-        *   Powered by Google's **Gemini 2.0 Flash** model.
-        *   Shows the model's **thoughts** before the final answer (experimental feature).
-        *   Supports **conversation history** for multi-turn chats.
-        *   Supports **Image and CSV file uploads** for analysis.
-        *   Uses **streaming** for a more interactive experience.
-        **Instructions:**
-        1.  Type your message in the input box or Upload a file below.
-        2.  Press Enter/Submit or Upload to send.
-        3.  Observe the chatbot's "Thinking" process followed by the final response.
-        4.  Use the "Clear Chat" button to start a new conversation.
-        *Please note*: The 'thinking' feature is experimental and the quality of thoughts may vary. File analysis capabilities may be limited depending on the model's experimental features.
-        """
-    )
-
-
-# Launch the interface
-if __name__ == "__main__":
-    demo.launch(debug=True)