Update app.py

app.py

@@ -1,64 +1,57 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_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]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from transformers import pipeline
+from PyPDF2 import PdfReader
+
+# Load a generative model for human-like answers
+question_answer_pipeline = pipeline("text2text-generation", model="google/flan-t5-large")
+
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(pdf_file_path):
+    try:
+        reader = PdfReader(pdf_file_path)
+        text = ""
+        for page in reader.pages:
+            page_text = page.extract_text()
+            if page_text:  # Check if text is extracted
+                text += page_text
+        return text.strip()
+    except Exception as e:
+        return f"Error extracting text from PDF: {e}"
+
+
+# Function to process the context and generate a human-like answer
+def get_humanlike_answer(pdf_path, text_input, question):
+    if pdf_path:  # If a PDF is uploaded
+        context = extract_text_from_pdf(pdf_path)
+        if context.startswith("Error"):
+            return context  # Return the error message if extraction failed
+    elif text_input.strip():  # If text is pasted
+        context = text_input
+    else:
+        return "Please upload a PDF or paste text for context."
+
+    # Generate a conversational answer
+    prompt = f"Context: {context}\nQuestion: {question}\nAnswer conversationally:"
+    try:
+        response = question_answer_pipeline(prompt, max_length=150, num_return_sequences=1)
+        return response[0]["generated_text"] if "generated_text" in response[0] else "Error: Could not generate an answer."
+    except Exception as e:
+        return f"Error generating answer: {e}"
+
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=get_humanlike_answer,
+    inputs=[
+        gr.File(label="Upload PDF (optional)", type="filepath"),
+        gr.Textbox(label="Paste Text (optional)", lines=10),
+        gr.Textbox(label="Ask a Question", lines=1),
     ],
+    outputs=gr.Textbox(label="Answer", lines=4),
+    title="PDF/Text Question Answering System",
+    description="Upload a PDF or paste text and ask questions. Get human-like answers! If both are provided, the PDF will be used."
 )
 
-
 if __name__ == "__main__":
     demo.launch()