Update app.py

app.py

@@ -1,166 +1,166 @@
-import gradio as gr
-import fitz  # PyMuPDF
-import os
-import tempfile
-import requests
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-from datetime import datetime
-
-# === CONFIG CHECK ===
-if not os.getenv("GROQ_API_KEY"):
-    print("WARNING: GROQ_API_KEY environment variable not set. API calls will fail.")
-
-# === Globals ===
-vectorizer = TfidfVectorizer(stop_words='english')
-
-# === UTILITY FUNCTIONS ===
-def call_groq_api(prompt):
-    api_key = os.getenv("GROQ_API_KEY")
-    if not api_key:
-        return "Error: GROQ_API_KEY environment variable not set."
-
-    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
-    data = {"model": "llama3-8b-8192", "messages": [{"role": "user", "content": prompt}]}
-
-    try:
-        response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers)
-        response.raise_for_status()
-        return response.json()["choices"][0]["message"]["content"]
-    except requests.exceptions.RequestException as e:
-        return f"API Error: {str(e)}"
-    except (KeyError, IndexError) as e:
-        return f"Error parsing API response: {str(e)}"
-
-def extract_text_from_pdfs(pdf_files):
-    chunks, pages, file_names = [], [], []
-    for file in pdf_files:
-        try:
-            doc = fitz.open(file.name)
-            for page_num, page in enumerate(doc, start=1):
-                text = page.get_text().strip()
-                if text:
-                    chunks.append(text)
-                    pages.append(page_num)
-                    file_names.append(os.path.basename(file.name))
-        except Exception as e:
-            print(f"Error processing {file.name}: {e}")
-    return chunks, pages, file_names
-
-def retrieve_context(query, chunks, pages, file_names, top_k=3):
-    all_texts = chunks + [query]
-    tfidf_matrix = vectorizer.fit_transform(all_texts)
-    query_vec = tfidf_matrix[-1]
-    similarities = cosine_similarity(query_vec, tfidf_matrix[:-1]).flatten()
-
-    if max(similarities) < 0.2:
-        return "Ask a relevant question.", [], []
-
-    top_indices = similarities.argsort()[-top_k:][::-1]
-    selected_chunks = [chunks[i] for i in top_indices]
-    references = [f"{file_names[i]} (p.{pages[i]})" for i in top_indices]
-    return "\n".join(selected_chunks), selected_chunks, references
-
-
-def download_chat(chat_history):
-    if not chat_history:
-        return None
-    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-    filename = f"chat_{timestamp}.txt"
-    path = os.path.join(tempfile.gettempdir(), filename)
-    with open(path, "w", encoding="utf-8") as f:
-        for q, a in chat_history:
-            f.write(f"Q: {q}\nA: {a}\n\n")
-    return path
-
-
-# === Main Q&A Logic ===
-def answer_question(text_input, pdf_files, chat_history):
-    if chat_history is None:
-        chat_history = []
-
-    if not text_input:
-        return "❗ Please type a question.", chat_history, chat_history
-    if not pdf_files:
-        return "❗ Please upload PDF files first.", chat_history, chat_history
-
-    chunks, pages, file_names = extract_text_from_pdfs(pdf_files)
-    if not chunks:
-        return "❗ Could not extract text from PDFs.", chat_history, chat_history
-
-    context, matched_chunks, references = retrieve_context(text_input, chunks, pages, file_names)
-
-    if context == "Ask a relevant question.":
-        response = "⚠️ Ask a relevant question based on the PDFs."
-        chat_history.append([text_input, response])
-        return response, chat_history, chat_history
-
-    prompt = f"Answer the question using this context:\n\n{context}\n\nQuestion: {text_input}\n\nAnswer:"
-    answer = call_groq_api(prompt)
-    full_answer = f"{answer}\n\n📌 Sources: {', '.join(references)}"
-    chat_history.append([text_input, full_answer])
-    return full_answer, chat_history, chat_history
-
-
-# === Custom CSS ===
-custom_css = """
-.gradio-container {
-    max-width: 900px !important;
-    margin: auto;
-    font-family: 'Segoe UI', sans-serif;
-}
-
-body {
-    background-color: var(--background-primary);
-    color: var(--body-text-color);
-}
-
-textarea, input, button {
-    font-family: 'Segoe UI', sans-serif !important;
-}
-"""
-
-# === Launch UI ===
-with gr.Blocks(css=custom_css, theme=gr.themes.Base()) as demo:
-    gr.Markdown("""
-    # 🧠 **SmartPDF Q&A Bot**  
-    _Ask questions from your PDFs. Get answers with page references. Download chat history._
-    """, elem_id="title")
-
-    chat_state = gr.State([])
-
-    with gr.Tabs():
-        with gr.Tab("📂 Upload PDFs"):
-            gr.Markdown("### Step 1: Upload one or more PDF documents.")
-            pdf_input = gr.File(label="📁 Upload PDF Files", file_types=[".pdf"], file_count="multiple")
-
-        with gr.Tab("💬 Ask Questions"):
-            gr.Markdown("### Step 2: Ask a question about the uploaded documents.")
-            with gr.Row():
-                text_input = gr.Textbox(label="❓ Type your question here", placeholder="e.g. What is the main idea of the first document?", lines=2)
-                ask_btn = gr.Button("🔍 Ask")
-
-            answer_output = gr.Textbox(label="🧠 Answer", lines=6)
-            chatbox = gr.Dataframe(headers=["User", "Bot"], label="💬 Chat History", interactive=False)
-
-        with gr.Tab("📥 Export Chat History"):
-            gr.Markdown("### Step 3: Download your chat session.")
-            download_btn = gr.Button("⬇️ Download Chat History")
-            download_file = gr.File(label="📄 Your Chat File", visible=False)
-
-    # === Button Event Binding ===
-    ask_btn.click(
-        answer_question,
-        inputs=[text_input, pdf_input, chat_state],
-        outputs=[answer_output, chatbox, chat_state]
-    )
-
-    download_btn.click(
-        download_chat,
-        inputs=[chat_state],
-        outputs=download_file
-    ).then(lambda: gr.update(visible=True), None, [download_file])
-
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import fitz  # PyMuPDF
+import os
+import tempfile
+import requests
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from datetime import datetime
+
+# === CONFIG CHECK ===
+if not os.getenv("GROQ_API_KEY"):
+    print("WARNING: GROQ_API_KEY environment variable not set. API calls will fail.")
+
+# === Globals ===
+vectorizer = TfidfVectorizer(stop_words='english')
+
+# === UTILITY FUNCTIONS ===
+def call_groq_api(prompt):
+    api_key = os.getenv("GROQ_API_KEY")
+    if not api_key:
+        return "Error: GROQ_API_KEY environment variable not set."
+
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    data = {"model": "llama3-8b-8192", "messages": [{"role": "user", "content": prompt}]}
+
+    try:
+        response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers)
+        response.raise_for_status()
+        return response.json()["choices"][0]["message"]["content"]
+    except requests.exceptions.RequestException as e:
+        return f"API Error: {str(e)}"
+    except (KeyError, IndexError) as e:
+        return f"Error parsing API response: {str(e)}"
+
+def extract_text_from_pdfs(pdf_files):
+    chunks, pages, file_names = [], [], []
+    for file in pdf_files:
+        try:
+            doc = fitz.open(file.name)
+            for page_num, page in enumerate(doc, start=1):
+                text = page.get_text().strip()
+                if text:
+                    chunks.append(text)
+                    pages.append(page_num)
+                    file_names.append(os.path.basename(file.name))
+        except Exception as e:
+            print(f"Error processing {file.name}: {e}")
+    return chunks, pages, file_names
+
+def retrieve_context(query, chunks, pages, file_names, top_k=3):
+    all_texts = chunks + [query]
+    tfidf_matrix = vectorizer.fit_transform(all_texts)
+    query_vec = tfidf_matrix[-1]
+    similarities = cosine_similarity(query_vec, tfidf_matrix[:-1]).flatten()
+
+    if max(similarities) < 0.2:
+        return "Ask a relevant question.", [], []
+
+    top_indices = similarities.argsort()[-top_k:][::-1]
+    selected_chunks = [chunks[i] for i in top_indices]
+    references = [f"{file_names[i]} (p.{pages[i]})" for i in top_indices]
+    return "\n".join(selected_chunks), selected_chunks, references
+
+
+def download_chat(chat_history):
+    if not chat_history:
+        return None
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    filename = f"chat_{timestamp}.txt"
+    path = os.path.join(tempfile.gettempdir(), filename)
+    with open(path, "w", encoding="utf-8") as f:
+        for q, a in chat_history:
+            f.write(f"Q: {q}\nA: {a}\n\n")
+    return path
+
+
+# === Main Q&A Logic ===
+def answer_question(text_input, pdf_files, chat_history):
+    if chat_history is None:
+        chat_history = []
+
+    if not text_input:
+        return "❗ Please type a question.", chat_history, chat_history
+    if not pdf_files:
+        return "❗ Please upload PDF files first.", chat_history, chat_history
+
+    chunks, pages, file_names = extract_text_from_pdfs(pdf_files)
+    if not chunks:
+        return "❗ Could not extract text from PDFs.", chat_history, chat_history
+
+    context, matched_chunks, references = retrieve_context(text_input, chunks, pages, file_names)
+
+    if context == "Ask a relevant question.":
+        response = "⚠️ Ask a relevant question based on the PDFs."
+        chat_history.append([text_input, response])
+        return response, chat_history, chat_history
+
+    prompt = f"Answer the question using this context:\n\n{context}\n\nQuestion: {text_input}\n\nAnswer:"
+    answer = call_groq_api(prompt)
+    full_answer = f"{answer}\n\n📌 Sources: {', '.join(references)}"
+    chat_history.append([text_input, full_answer])
+    return full_answer, chat_history, chat_history
+
+
+# === Custom CSS ===
+custom_css = """
+.gradio-container {
+    max-width: 900px !important;
+    margin: auto;
+    font-family: 'Segoe UI', sans-serif;
+}
+
+body {
+    background-color: var(--background-primary);
+    color: var(--body-text-color);
+}
+
+textarea, input, button {
+    font-family: 'Segoe UI', sans-serif !important;
+}
+"""
+
+# === Launch UI ===
+with gr.Blocks(css=custom_css, theme=gr.themes.Base()) as demo:
+    gr.Markdown("""
+    # 🧠 **SmartPDF Q&A Bot**  
+    _Ask questions from your PDFs. Get answers with page references. Download chat history._
+    """, elem_id="title")
+
+    chat_state = gr.State([])
+
+    with gr.Tabs():
+        with gr.Tab("📂 Upload PDFs"):
+            gr.Markdown("### Step 1: Upload one or more PDF documents.")
+            pdf_input = gr.File(label="📁 Upload PDF Files", file_types=[".pdf"], file_count="multiple")
+
+        with gr.Tab("💬 Ask Questions"):
+            gr.Markdown("### Step 2: Ask a question about the uploaded documents.")
+            with gr.Row():
+                text_input = gr.Textbox(label="❓ Type your question here", placeholder="e.g. What is the main idea of the first document?", lines=2)
+                ask_btn = gr.Button("🔍 Ask")
+
+            answer_output = gr.Textbox(label="🧠 Answer", lines=6)
+            chatbox = gr.Dataframe(headers=["User", "Bot"], label="💬 Chat History", interactive=False)
+
+        with gr.Tab("📥 Export Chat History"):
+            gr.Markdown("### Step 3: Download your chat session.")
+            download_btn = gr.Button("⬇️ Download Chat History")
+            download_file = gr.File(label="📄 Your Chat File", visible=False)
+
+    # === Button Event Binding ===
+    ask_btn.click(
+        answer_question,
+        inputs=[text_input, pdf_input, chat_state],
+        outputs=[answer_output, chatbox, chat_state]
+    )
+
+    download_btn.click(
+        download_chat,
+        inputs=[chat_state],
+        outputs=download_file
+    ).then(lambda: gr.update(visible=True), None, [download_file])
+
+
+if __name__ == "__main__":
+    demo.launch(share=True)