fixing not found problem

app.py

@@ -1,188 +1,105 @@
 import urllib.request
-import fitz
+import fitz  # PyMuPDF
 import re
 import numpy as np
 import tensorflow_hub as hub
-import openai
-import gradio as gr
-import os
 from sklearn.neighbors import NearestNeighbors
+import os
+import gradio as gr
 
 def download_pdf(url, output_path):
-    urllib.request.urlretrieve(url, output_path)
-
+    try:
+        urllib.request.urlretrieve(url, output_path)
+        return True
+    except Exception as e:
+        print(f"Error downloading PDF: {e}")
+        return False
 
 def preprocess(text):
     text = text.replace('\n', ' ')
-    text = re.sub('\s+', ' ', text)
+    text = re.sub('\s+', ' ', text).strip()
     return text
 
-
 def pdf_to_text(path, start_page=1, end_page=None):
-    doc = fitz.open(path)
-    total_pages = doc.page_count
+    try:
+        doc = fitz.open(path)
+        total_pages = doc.page_count
 
-    if end_page is None:
-        end_page = total_pages
+        if end_page is None:
+            end_page = total_pages
 
-    text_list = []
+        text_list = []
 
-    for i in range(start_page-1, end_page):
-        text = doc.load_page(i).get_text("text")
-        text = preprocess(text)
-        text_list.append(text)
-
-    doc.close()
-    return text_list
+        for i in range(start_page - 1, end_page):
+            page = doc.load_page(i)
+            text = page.get_text("text")
+            text = preprocess(text)
+            text_list.append(text)
 
+        doc.close()
+        return text_list
+    except Exception as e:
+        print(f"Error in PDF to text conversion: {e}")
+        return None
 
 def text_to_chunks(texts, word_length=150, start_page=1):
-    text_toks = [t.split(' ') for t in texts]
-    page_nums = []
+    text_tokens = [t.split(' ') for t in texts]
     chunks = []
-    
-    for idx, words in enumerate(text_toks):
+
+    for idx, words in enumerate(text_tokens):
         for i in range(0, len(words), word_length):
-            chunk = words[i:i+word_length]
-            if (i+word_length) > len(words) and (len(chunk) < word_length) and (
-                len(text_toks) != (idx+1)):
-                text_toks[idx+1] = chunk + text_toks[idx+1]
-                continue
+            chunk = words[i:i + word_length]
             chunk = ' '.join(chunk).strip()
-            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
+            chunk = f'[Page no. {idx + start_page}] ' + chunk
             chunks.append(chunk)
+            
     return chunks
 
-
 class SemanticSearch:
-    
     def __init__(self):
         self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
         self.fitted = False
-    
-    
-    def fit(self, data, batch=1000, n_neighbors=5):
+
+    def fit(self, data):
         self.data = data
-        self.embeddings = self.get_text_embedding(data, batch=batch)
-        n_neighbors = min(n_neighbors, len(self.embeddings))
-        self.nn = NearestNeighbors(n_neighbors=n_neighbors)
+        self.embeddings = self.use(data)
+        self.nn = NearestNeighbors(n_neighbors=5)
         self.nn.fit(self.embeddings)
         self.fitted = True
-    
-    
-    def __call__(self, text, return_data=True):
-        inp_emb = self.use([text])
-        neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
-        
-        if return_data:
-            return [self.data[i] for i in neighbors]
-        else:
-            return neighbors
-    
-    
-    def get_text_embedding(self, texts, batch=1000):
-        embeddings = []
-        for i in range(0, len(texts), batch):
-            text_batch = texts[i:(i+batch)]
-            emb_batch = self.use(text_batch)
-            embeddings.append(emb_batch)
-        embeddings = np.vstack(embeddings)
-        return embeddings
-
-
-
-def load_recommender(path, start_page=1):
-    global recommender
-    texts = pdf_to_text(path, start_page=start_page)
-    chunks = text_to_chunks(texts, start_page=start_page)
-    recommender.fit(chunks)
-    return 'Corpus Loaded.'
-
 
-def generate_text(prompt, engine="text-davinci-003"):
-    openai.api_key = os.getenv('Your_Key_Here')
-    completions = openai.Completion.create(
-        engine=engine,
-        prompt=prompt,
-        max_tokens=512,
-        n=1,
-        stop=None,
-        temperature=0.7,
-    )
-    message = completions.choices[0].text
-    return message
-
-
-def generate_answer(question):
-    topn_chunks = recommender(question)
-    prompt = ""
-    prompt += 'search results:\n\n'
-    for c in topn_chunks:
-        prompt += c + '\n\n'
+    def __call__(self, text):
+        if not self.fitted:
+            return "Model not fitted yet."
         
-    prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
-              "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "\
-              "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\
-              "with the same name, create separate answers for each. Only include information found in the results and "\
-              "don't add any additional information. Make sure the answer is correct and don't output false content. "\
-              "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\
-              "search results which has nothing to do with the question. Only answer what is asked. The "\
-              "answer should be short and concise. \n\nQuery: {question}\nAnswer: "
-    
-    prompt += f"Query: {question}\nAnswer:"
-    answer = generate_text(prompt,"text-davinci-003")
-    return answer
-
-
-def question_answer(url, file, question):
-    if url.strip() == '' and file == None:
-        return '[ERROR]: Both URL and PDF is empty. Provide atleast one.'
-    
-    if url.strip() != '' and file != None:
-        return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).'
-
-    if url.strip() != '':
-        glob_url = url
-        download_pdf(glob_url, 'corpus.pdf')
-        load_recommender('corpus.pdf')
-
-    else:
-        old_file_name = file.name
-        file_name = file.name
-        file_name = file_name[:-12] + file_name[-4:]
-        os.rename(old_file_name, file_name)
-        load_recommender(file_name)
-
-    if question.strip() == '':
-        return '[ERROR]: Question field is empty'
-
-    return generate_answer(question)
-
+        query_embedding = self.use([text])
+        neighbors = self.nn.kneighbors(query_embedding, return_distance=False)[0]
+        return [self.data[i] for i in neighbors]
 
 recommender = SemanticSearch()
 
-title = 'CauseWriter PDF'
-description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
-
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(f'<center><h1>{title}</h1></center>')
-    gr.Markdown(description)
-
-    with gr.Row():
+def gui(url, question):
+    if url.strip():
+        if not download_pdf(url, "temp.pdf"):
+            return "Failed to download PDF."
         
-        with gr.Group():
-            url = gr.Textbox(label='Enter PDF URL here')
-            gr.Markdown("<center><h4>OR<h4></center>")
-            file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
-            question = gr.Textbox(label='Enter your question here')
-            btn = gr.Button(value='Submit')
-            btn.style(full_width=True)
-
-        with gr.Group():
-            answer = gr.Textbox(label='The answer to your question is :')
-
-        btn.click(question_answer, inputs=[url, file, question], outputs=[answer])
-
-demo.launch()
+        texts = pdf_to_text("temp.pdf")
+        if texts is None:
+            return "Failed to extract text from PDF."
+        
+        chunks = text_to_chunks(texts)
+        recommender.fit(chunks)
+    else:
+        return "Please provide a valid URL."
+    
+    if question.strip():
+        results = recommender(question)
+        return results
+    else:
+        return "Please enter a question."
+
+iface = gr.Interface(
+    fn=gui,
+    inputs=["text", "text"],
+    outputs="text"
+)
+iface.launch()