revoked

app.py

@@ -1,28 +1,10 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import pymupdf
-from duckduckgo_search import DDGS
 
 """
 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("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-
-# PDF Parsing
-def extract_text_from_pdf(pdf_file):
-    doc = pymupdf.open(pdf_file)
-    text = " ".join([page.get_textpage().extractTEXT() for page in doc])
-    return text
-
-
-# Web search fallback
-def search_web(query):
-    with DDGS() as ddgs:
-        results = ddgs.text(query)
-        if results:
-            return results[0]["body"]
-    return "No relevant results found on the web."
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 def respond(
@@ -32,51 +14,19 @@ def respond(
     max_tokens,
     temperature,
     top_p,
-    pdf_file=None,
 ):
-    recent_keywords = [
-        "latest",
-        "today",
-        "current",
-        "now",
-        "recent",
-        "news",
-        "update",
-        "price",
-        "who won",
-        "what happened",
-    ]
-    message_lower = message.lower()
-
-    # Check if this is a recent/live query
-    if any(kw in message_lower for kw in recent_keywords):
-        web_result = search_web(message)
-        if web_result:
-            yield f"[Answer from Web Search]\n{web_result}"
-            return
-
-    # Check if PDF was uploaded
-    context_text = ""
-    if pdf_file is not None:
-        try:
-            context_text = extract_text_from_pdf(pdf_file.name)
-        except Exception as e:
-            yield f"[Error reading PDF: {str(e)}]"
+    messages = [{"role": "system", "content": system_message}]
 
-    # Try answering from PDF if context exists
-    if context_text.strip():
-        message = f"Given the following document context:\n\n{context_text[:2000]}\n\nAnswer the question:\n{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]})
 
-    # Fallback to LLM (Zephyr)
-    messages = [{"role": "system", "content": system_message}]
-    for user, assistant in history:
-        if user:
-            messages.append({"role": "user", "content": user})
-        if assistant:
-            messages.append({"role": "assistant", "content": assistant})
     messages.append({"role": "user", "content": message})
 
     response = ""
+
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -85,7 +35,8 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-        response += token if token is not None else ""
+
+        response += token
         yield response
 
 
@@ -105,7 +56,6 @@ demo = gr.ChatInterface(
             step=0.05,
             label="Top-p (nucleus sampling)",
         ),
-        gr.File(label="Upload a PDF", file_types=[".pdf"]),
     ],
 )
 

requirements.txt

@@ -1,3 +1 @@
-huggingface_hub==0.25.2
-duckduckgo_search
-pymupdf
+huggingface_hub==0.25.2