Update app.py

app.py

@@ -1,53 +1,140 @@
-import gradio as gr
-from google import generativeai as genai
 import os
+import gradio as gr
+import requests
+import hashlib
+from functools import lru_cache
+from docx import Document
+import PyPDF2
+import textract
+
+# Check if the API key exists in environment variables
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    raise ValueError("GROQ_API_KEY not found in environment variables. Please add it to Hugging Face Secrets.")
+
+# Function to generate hash of data directory contents
+def get_data_hash():
+    data_dir = "data"
+    if not os.path.exists(data_dir):
+        return ""
+    hasher = hashlib.sha256()
+    try:
+        for root, dirs, files in os.walk(data_dir):
+            for file in sorted(files):
+                filepath = os.path.join(root, file)
+                if os.path.isfile(filepath):
+                    with open(filepath, 'rb') as f:
+                        hasher.update(f.read())
+        return hasher.hexdigest()
+    except Exception as e:
+        print(f"Error hashing files: {e}")
+        return ""
+
+# Cache business info processing with hash-based invalidation
+@lru_cache(maxsize=1)
+def read_business_info(data_hash):
+    business_info = []
+    data_dir = "data"
+    
+    if not os.path.exists(data_dir):
+        return "Data directory not found. Please upload a 'data' folder with relevant files."
+    
+    supported_extensions = ['.txt', '.pdf', '.docx', '.doc']
+    
+    for filename in os.listdir(data_dir):
+        filepath = os.path.join(data_dir, filename)
+        ext = os.path.splitext(filename)[1].lower()
+        
+        if ext not in supported_extensions:
+            continue  # Skip unsupported files
+        
+        try:
+            if ext == '.txt':
+                with open(filepath, 'r', encoding='utf-8') as f:
+                    business_info.append(f.read())
+            elif ext == '.pdf':
+                with open(filepath, 'rb') as f:
+                    reader = PyPDF2.PdfReader(f)
+                    text = '\n'.join([page.extract_text() for page in reader.pages])
+                    business_info.append(text)
+            elif ext == '.docx':
+                doc = Document(filepath)
+                text = '\n'.join([para.text for para in doc.paragraphs])
+                business_info.append(text)
+            elif ext == '.doc':
+                text = textract.process(filepath).decode('utf-8')
+                business_info.append(text)
+        except Exception as e:
+            business_info.append(f"Error reading {filename}: {str(e)}")
+    
+    if not business_info:
+        return "No valid files found in the data directory."
+    
+    return '\n\n'.join(business_info)
+
+# Function to generate response using Groq's LLaMA 3 70B API
+def generate_response(message, chat_history):
+    current_hash = get_data_hash()
+    business_info = read_business_info(current_hash)
+    
+    # Create system prompt including business information
+    system_prompt = f"""You are a helpful business assistant that answers questions about a specific business.
+    
+Business Information:
+{business_info}
+Answer ONLY using information from the business information above. If the question cannot be answered using the provided business information, respond with "Yeh information abhi available nahi hai."
+You can respond in Hindi-English mix (Hinglish) if the user asks in that format. Be concise and helpful."""
+
+    # Prepare conversation history for the API
+    messages = [{"role": "system", "content": system_prompt}]
+    
+    # Add conversation history
+    for user_msg, assistant_msg in chat_history:
+        messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:  # Only add if not None
+            messages.append({"role": "assistant", "content": assistant_msg})
+    
+    # Add the current message
+    messages.append({"role": "user", "content": message})
+    
+    # Make API call to Groq
+    try:
+        response = requests.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {GROQ_API_KEY}",
+                "Content-Type": "application/json"
+            },
+            json={
+                "model": "llama3-70b-8192",
+                "messages": messages,
+                "temperature": 0.7,
+                "max_tokens": 500
+            },
+            timeout=60
+        )
+        
+        if response.status_code == 200:
+            return response.json()["choices"][0]["message"]["content"]
+        else:
+            return f"Error: {response.status_code} - {response.text}"
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Create a simple Gradio chat interface
+def respond(message, history):
+    response = generate_response(message, history)
+    return response
 
-# — Load and configure Gemini API key from HF Secrets
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-if not gemini_api_key:
-    raise ValueError("GEMINI_API_KEY not set in environment")
-genai.configure(api_key=gemini_api_key)  # sets auth for all calls :contentReference[oaicite:2]{index=2}
-
-# — Path to your uploaded business.txt
-business_file = os.path.join(os.path.dirname(__file__), "business.txt")
-
-def chat_with_business(message, history):
-    # 1️⃣ Read business info
-    with open(business_file, "r", encoding="utf-8") as f:
-        business_info = f.read().strip()
-
-    # 2️⃣ Build system prompt
-    system_prompt = (
-        "You are a helpful customer-care assistant. "
-        "Use only the information below to answer questions. "
-        "If the answer is not present, reply 'Yeh information abhi available nahi hai.'\n\n"
-        f"{business_info}\n\n"
-    )
-
-    # 3️⃣ Call Gemini 2.5 Flash
-    model = genai.GenerativeModel(model_name="gemini-2.5-flash-preview-04-17")
-    response = model.generate_content(system_prompt + "User: " + message)  # :contentReference[oaicite:3]{index=3}
-
-    # 4️⃣ Return text
-    return response.text
-
-# — Build Gradio UI with Blocks and messages format
-with gr.Blocks(theme="soft") as demo:
-    gr.Markdown("## 🌿 My Business Bot")
-    gr.Markdown("*Ask anything about your business in Hindi-English*")
-    chatbot = gr.Chatbot(elem_id="chatbox", height=400, type="messages")  # use messages format :contentReference[oaicite:4]{index=4}
-    user_input = gr.Textbox(placeholder="Type your question here...", show_label=False)
-
-    def handle(msg, hist):
-        reply = chat_with_business(msg, hist)
-        # Append OpenAI-style dicts, not tuples :contentReference[oaicite:5]{index=5}
-        hist = hist + [
-            {"role": "user",      "content": msg},
-            {"role": "assistant", "content": reply}
-        ]
-        return hist, ""
-
-    user_input.submit(handle, [user_input, chatbot], [chatbot, user_input])
+demo = gr.ChatInterface(
+    fn=respond,
+    title="🌿 My Business Bot",
+    description="Ask anything about your business in Hindi-English",
+    theme=gr.themes.Soft(),
+    examples=["What are your business hours?", "कीमत क्या है?", "Tell me about your products", "Return policy kya hai?"],
+    cache_examples=False  # Disable Gradio's example caching
+)
 
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()