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README.md

@@ -4,8 +4,8 @@ emoji: 🤖
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.36.2
-app_file: app.py
+sdk_version: 4.0.0
+app_file: app_simple.py
 pinned: false
 ---
 
@@ -51,7 +51,7 @@ export OPENAI_API_KEY="your-api-key-here"
 
 4. Run the application:
 ```bash
-python app.py
+python app_simple.py
 ```
 
 The chatbot will be available at `http://localhost:7860`
@@ -76,7 +76,7 @@ git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
 
 3. Copy your files to the Space repository:
 ```bash
-cp app.py requirements.txt README.md /path/to/space/repo/
+cp app_simple.py requirements.txt README.md "Health Tech Hub Copenhagen.pdf" /path/to/space/repo/
 ```
 
 4. Push to Hugging Face:
@@ -95,7 +95,7 @@ Make sure to set the following environment variable in your Hugging Face Space:
 
 ## Configuration
 
-You can customize the chatbot by modifying the following parameters in `app.py`:
+You can customize the chatbot by modifying the following parameters in `app_simple.py`:
 
 - **Model**: Change `gpt-3.5-turbo` to other OpenAI models
 - **Max tokens**: Adjust `max_tokens` for response length
@@ -121,4 +121,4 @@ This project is open source and available under the MIT License.
 
 ## Contributing
 
-Feel free to submit issues and enhancement requests!
+Feel free to submit issues and enhancement requests! 

app_simple.py

@@ -0,0 +1,192 @@
+import gradio as gr
+import openai
+import os
+import PyPDF2
+import re
+from typing import List, Tuple
+
+# Configuration
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-3.5-turbo")
+MAX_TOKENS = int(os.getenv("MAX_TOKENS", "500"))
+TEMPERATURE = float(os.getenv("TEMPERATURE", "0.7"))
+
+# Validate API key
+if not OPENAI_API_KEY:
+    print("❌ OPENAI_API_KEY environment variable is required")
+    exit(1)
+
+# Initialize OpenAI client
+client = openai.OpenAI(api_key=OPENAI_API_KEY)
+
+# Simple PDF processing
+class SimplePDFHelper:
+    def __init__(self, pdf_path="Health Tech Hub Copenhagen.pdf"):
+        self.pdf_path = pdf_path
+        self.pdf_text = ""
+        self.loaded = False
+        
+    def load_pdf(self):
+        """Load and extract text from PDF"""
+        if self.loaded:
+            return True
+            
+        try:
+            if not os.path.exists(self.pdf_path):
+                print(f"⚠️ PDF file not found: {self.pdf_path}")
+                return False
+                
+            text = ""
+            with open(self.pdf_path, 'rb') as file:
+                pdf_reader = PyPDF2.PdfReader(file)
+                for page in pdf_reader.pages:
+                    text += page.extract_text() + "\n"
+            
+            self.pdf_text = text
+            self.loaded = True
+            print(f"✅ PDF loaded successfully ({len(text)} characters)")
+            return True
+            
+        except Exception as e:
+            print(f"❌ Error loading PDF: {e}")
+            return False
+    
+    def search_text(self, query: str, max_length: int = 500) -> str:
+        """Simple text search in PDF content"""
+        if not self.loaded:
+            if not self.load_pdf():
+                return ""
+        
+        # Simple keyword search
+        query_words = set(re.findall(r'\b\w+\b', query.lower()))
+        
+        # Split text into sentences
+        sentences = re.split(r'[.!?]+', self.pdf_text)
+        
+        # Find sentences with matching keywords
+        relevant_sentences = []
+        for sentence in sentences:
+            sentence_words = set(re.findall(r'\b\w+\b', sentence.lower()))
+            if query_words.intersection(sentence_words):
+                relevant_sentences.append(sentence.strip())
+        
+        # Return first few relevant sentences
+        if relevant_sentences:
+            result = ". ".join(relevant_sentences[:3]) + "."
+            return result[:max_length]
+        
+        return ""
+
+# Initialize PDF helper
+pdf_helper = SimplePDFHelper()
+
+def chat_with_bot(message: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
+    """Chat function with simple PDF integration"""
+    if not message.strip():
+        return "", history
+    
+    # Search for relevant PDF content
+    pdf_context = pdf_helper.search_text(message)
+    
+    # Prepare system message
+    system_message = (
+        "You are a helpful AI assistant with knowledge about Health Tech Hub Copenhagen. "
+        "Use the provided PDF information when relevant to answer questions accurately. "
+        "Keep your responses concise and engaging."
+    )
+    
+    # Prepare conversation history
+    messages = [{"role": "system", "content": system_message}]
+    
+    # Add conversation history
+    for human, assistant in history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": assistant})
+    
+    # Add current message with PDF context
+    full_message = message
+    if pdf_context:
+        full_message = f"{message}\n\nRelevant information from the Health Tech Hub Copenhagen document:\n{pdf_context}"
+    
+    messages.append({"role": "user", "content": full_message})
+    
+    try:
+        # Get response from OpenAI
+        response = client.chat.completions.create(
+            model=OPENAI_MODEL,
+            messages=messages,
+            max_tokens=MAX_TOKENS,
+            temperature=TEMPERATURE
+        )
+        
+        assistant_response = response.choices[0].message.content
+        
+        # Update history
+        history.append((message, assistant_response))
+        
+        return "", history
+        
+    except Exception as e:
+        error_message = f"Sorry, I encountered an error: {str(e)}"
+        history.append((message, error_message))
+        return "", history
+
+def clear_chat():
+    """Clear the chat history"""
+    return []
+
+# Create Gradio interface
+with gr.Blocks(
+    title="AI Chatbot with PDF Knowledge",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {
+        max-width: 800px;
+        margin: auto;
+    }
+    """
+) as demo:
+    gr.Markdown(
+        """
+        # 🤖 AI Chatbot with PDF Knowledge
+        
+        Welcome! I'm your AI assistant with knowledge about Health Tech Hub Copenhagen. 
+        I can answer questions based on the PDF document and provide helpful information!
+        
+        ---
+        """
+    )
+    
+    # Chat interface
+    chatbot = gr.Chatbot(
+        height=500,
+        show_label=False,
+        container=True,
+        bubble_full_width=False
+    )
+    
+    # Message input
+    msg = gr.Textbox(
+        placeholder="Type your message here...",
+        show_label=False,
+        container=False
+    )
+    
+    # Clear button
+    clear = gr.Button("Clear Chat", variant="secondary")
+    
+    # Set up event handlers
+    msg.submit(
+        chat_with_bot,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot]
+    )
+    
+    clear.click(
+        clear_chat,
+        outputs=chatbot
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch() 

requirements.txt

@@ -1,6 +1,4 @@
 gradio>=4.0.0
 openai>=1.0.0
 python-dotenv>=1.0.0
-PyPDF2>=3.0.0
-langchain>=0.1.0
-langchain-openai>=0.1.0 
+PyPDF2>=3.0.0