Update app.py

app.py

@@ -1,52 +1,81 @@
 import os
 import docx
 import requests
-import pandas as pd
 import gradio as gr
+from io import BytesIO
 
 # ✅ API Key from Hugging Face Secrets
 API_KEY = os.getenv("GEMINI_API_KEY")  # Set this in Hugging Face secrets!
 
-# ✅ Set the File Path (Downloaded from Google Drive)
-FILE_PATH = "/content/drive/MyDrive/Structure analysis/CEP SA 22 (G1).docx"  # This should match the file downloaded in `download_file.py`
+# ✅ Function to Download File from Link
+def download_file_from_link(file_link):
+    """Download a file from the provided link."""
+    try:
+        response = requests.get(file_link)
+        response.raise_for_status()  # Raise an error for bad status codes
+        return BytesIO(response.content)  # Return file content as a BytesIO object
+    except Exception as e:
+        return f"❌ Error downloading file: {e}"
 
 # ✅ Function to Load and Read File Contents
-def load_data():
+def load_data(file_link):
     """Read content from the downloaded file."""
-    if not os.path.exists(FILE_PATH):
-        return "❌ File not found! Run `download_file.py` first."
+    file_content = download_file_from_link(file_link)
+    if isinstance(file_content, str):  # If an error message is returned
+        return file_content
 
-    doc = docx.Document(FILE_PATH)
-    return "\n".join([para.text for para in doc.paragraphs if para.text.strip()])
+    try:
+        doc = docx.Document(file_content)
+        return "\n".join([para.text for para in doc.paragraphs if para.text.strip()])
+    except Exception as e:
+        return f"❌ Error reading file: {e}"
 
 # ✅ Function to Call Google AI Gemini API
-def call_gemini_api(user_input):
+def call_gemini_api(file_link, user_input):
     """Send user query + document content to Google AI."""
     if not API_KEY:
         return "❌ Error: API Key not found! Add it as GEMINI_API_KEY in Hugging Face secrets."
 
     # Read document content
-    document_content = load_data()
+    document_content = load_data(file_link)
+    if document_content.startswith("❌"):
+        return document_content  # Return error message if file loading failed
+
+    # Truncate document content to avoid exceeding API limits
+    max_context_length = 4000  # Adjust based on API limits
+    truncated_content = document_content[:max_context_length]
 
     # Construct API request
     url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key={API_KEY}"
     headers = {"Content-Type": "application/json"}
-    payload = {"contents": [{"parts": [{"text": f"Document Context: {document_content[:4000]}\nUser Query: {user_input}"}]}]}
+    payload = {
+        "contents": [
+            {
+                "parts": [
+                    {"text": f"Document Context: {truncated_content}\nUser Query: {user_input}"}
+                ]
+            }
+        ]
+    }
 
     # Make API call
-    response = requests.post(url, json=payload, headers=headers)
-    if response.status_code == 200:
+    try:
+        response = requests.post(url, json=payload, headers=headers)
+        response.raise_for_status()  # Raise an error for bad status codes
         return response.json().get("candidates", [{}])[0].get("content", "No response from API")
-    else:
-        return f"❌ API Error: {response.status_code}, {response.text}"
+    except Exception as e:
+        return f"❌ API Error: {e}"
 
 # ✅ Gradio Interface
 iface = gr.Interface(
     fn=call_gemini_api,
-    inputs=gr.Textbox(label="Ask a question"),
+    inputs=[
+        gr.Textbox(label="File Link", placeholder="Paste the file link here (e.g., Google Drive link)"),
+        gr.Textbox(label="Ask a question")
+    ],
     outputs="text",
-    title="📄 AI Chatbot with Document Context",
-    description="This chatbot answers questions based on the document fetched from Google Drive."
+    title="📄 AI Chatbot with Real-Time Document Context",
+    description="This chatbot answers questions based on the document fetched from the provided link."
 )
 
 iface.launch()