iop

app.py

@@ -80,6 +80,8 @@ Deux axes de lecture distincts.
 Chaque axe subdivisé en deux sous-axes.
 Chaque sous-axe analysé via trois outils d'analyse spécifiques, nommés individuellement avec leur terme technique reconnu et officiel.
 Résultats présentés en tableau(x) ("Outils d'analyse", "Repérage / Citation", "Interprétation"), en respectant la règle un seul outil par entrée dans la première colonne.
+
+Note : Si plusieurs images sont fournies, analysez le texte de manière cohérente à travers toutes les images, en considérant l'ensemble comme un texte continu ou relié.
 """
 
 prompt_redaction = """
@@ -147,17 +149,18 @@ MODEL_ID_DEEPTHINK = "gemini-2.5-flash" # Advanced model for DeepThink
 # --- End Model IDs ---
 
 # --- Stream Generation Functions ---
-def generate_table_stream(image, consignes="", model_id=MODEL_ID_STANDARD):
-    """Génère le tableau d'analyse à partir de l'image en intégrant éventuellement des consignes"""
+def generate_table_stream(uploaded_files, consignes="", model_id=MODEL_ID_STANDARD):
+    """Génère le tableau d'analyse à partir des images uploadées en intégrant éventuellement des consignes"""
     prompt = prompt_tableau
     if consignes:
         prompt += "\n\nConsignes supplémentaires de l'utilisateur :\n" + consignes # Make consignes clearer in prompt
 
-    logging.info(f"Generating table using model: {model_id}")
+    logging.info(f"Generating table using model: {model_id} with {len(uploaded_files)} images")
     try:
+        contents = [prompt] + uploaded_files
         response_stream = genai_client.models.generate_content_stream(
-            model=model_id, # Use the passed model_id
-            contents=[prompt, image],
+            model=model_id,
+            contents=contents,
             config=generate_config
         )
 
@@ -212,11 +215,15 @@ def index():
 
 @app.route('/analyze', methods=['POST'])
 def analyze():
-    if 'image' not in request.files:
-        logging.warning("Analyze request received without image file.")
-        return jsonify({'error': 'No image uploaded'}), 400
+    if 'images' not in request.files:
+        logging.warning("Analyze request received without image files.")
+        return jsonify({'error': 'No images uploaded'}), 400
+
+    image_files = request.files.getlist('images')
+    if not image_files or all(f.filename == '' for f in image_files):
+        logging.warning("No valid image files provided.")
+        return jsonify({'error': 'No valid images uploaded'}), 400
 
-    image_file = request.files['image']
     consignes = request.form.get("consignes", "")
     # Check for the DeepThink flag
     use_deepthink = request.form.get('use_deepthink', 'false').lower() == 'true'
@@ -229,110 +236,111 @@ def analyze():
         model_id = MODEL_ID_STANDARD
         logging.info("Standard analysis requested, using standard model.")
 
-    # Use a temporary file to handle the image upload
-    temp_file = None # Initialize to None
+    # Handle multiple image uploads using File API
+    temp_files = []
+    uploaded_files = []
     try:
-        # Create a temporary file with a specific suffix if needed, or let NamedTemporaryFile handle it
-        # Using 'delete=False' requires manual cleanup
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+        for image_file in image_files:
+            # Create temporary file for each image
+            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
+            temp_files.append(temp_file)
             image_file.save(temp_file.name)
-            # Ensure image is valid before proceeding
+            temp_file.close()  # Close file handle so it can be read by PIL and uploaded
+
+            # Verify image is valid
             try:
                 image = Image.open(temp_file.name)
-                image.verify() # Verify image header
-                # Re-open after verify
-                image = Image.open(temp_file.name)
+                image.verify()
+                image.close()
             except (IOError, SyntaxError) as e:
                 logging.error(f"Invalid image file uploaded: {e}")
                 return jsonify({'error': f'Invalid or corrupted image file: {e}'}), 400
 
-            # Now 'image' holds the PIL Image object
-            # We need to pass the image object to the stream function
-            # Note: google.generativeai often works directly with PIL Image objects
-
-            @stream_with_context
-            def generate():
-                temp_file_path = temp_file.name # Store path for finally block
-                full_tableau_content = "" # Accumulate full table content for dissertation
-                try:
-                    logging.info("Starting table generation stream...")
-                    # Phase 1: Génération du tableau, passing the selected model_id
-                    for chunk_json in generate_table_stream(image, consignes, model_id):
+            # Upload to Gemini File API
+            try:
+                uploaded_file = genai_client.files.upload(file=temp_file.name)
+                uploaded_files.append(uploaded_file)
+                logging.info(f"Uploaded image to Gemini File API: {uploaded_file.name}")
+            except Exception as e:
+                logging.error(f"Error uploading to Gemini File API: {e}")
+                return jsonify({'error': f'Error uploading image to Gemini: {e}'}), 500
+
+        @stream_with_context
+        def generate():
+            full_tableau_content = ""
+            try:
+                logging.info("Starting table generation stream...")
+                # Phase 1: Génération du tableau, passing the selected model_id and uploaded files
+                for chunk_json in generate_table_stream(uploaded_files, consignes, model_id):
+                    try:
+                        chunk_data = json.loads(chunk_json)
+                        if chunk_data.get("type") == "error":
+                            logging.error(f"Error received from table stream: {chunk_data.get('error')}")
+                            yield chunk_json
+                            return
+                        elif chunk_data.get("type") == "tableau":
+                            full_tableau_content += chunk_data.get("chunk", "")
+                            yield chunk_json
+                    except json.JSONDecodeError:
+                          logging.error(f"Received invalid JSON from table stream: {chunk_json}")
+                          yield json.dumps({"type": "error", "error": "Invalid data received during table generation."}) + "\n"
+                          return
+
+                logging.info("Table generation stream finished.")
+                logging.info("Starting dissertation generation stream...")
+
+                # Phase 2: Génération de la dissertation basée sur le tableau COMPLET
+                if full_tableau_content:
+                    for chunk_json in generate_dissertation_stream(full_tableau_content, model_id):
                         try:
                             chunk_data = json.loads(chunk_json)
                             if chunk_data.get("type") == "error":
-                                logging.error(f"Error received from table stream: {chunk_data.get('error')}")
-                                yield chunk_json # Forward the error to the client
-                                return # Stop generation if table fails
-                            elif chunk_data.get("type") == "tableau":
-                                full_tableau_content += chunk_data.get("chunk", "")
-                                yield chunk_json # Stream chunk to client
+                                  logging.error(f"Error received from dissertation stream: {chunk_data.get('error')}")
+                                  yield chunk_json
+                            elif chunk_data.get("type") == "dissertation":
+                                  yield chunk_json
                         except json.JSONDecodeError:
-                             logging.error(f"Received invalid JSON from table stream: {chunk_json}")
-                             # Decide how to handle: yield error, ignore, etc.
-                             yield json.dumps({"type": "error", "error": "Invalid data received during table generation."}) + "\n"
-                             return # Stop if data is corrupt
-
-                    logging.info("Table generation stream finished.")
-                    logging.info("Starting dissertation generation stream...")
-
-                    # Phase 2: Génération de la dissertation basée sur le tableau COMPLET, passing the selected model_id
-                    if full_tableau_content: # Only generate if table content exists
-                        for chunk_json in generate_dissertation_stream(full_tableau_content, model_id):
-                            try:
-                                chunk_data = json.loads(chunk_json)
-                                if chunk_data.get("type") == "error":
-                                     logging.error(f"Error received from dissertation stream: {chunk_data.get('error')}")
-                                     yield chunk_json # Forward the error
-                                     # Decide if you want to return here or let it finish
-                                elif chunk_data.get("type") == "dissertation":
-                                     yield chunk_json # Stream chunk to client
-                            except json.JSONDecodeError:
-                                logging.error(f"Received invalid JSON from dissertation stream: {chunk_json}")
-                                yield json.dumps({"type": "error", "error": "Invalid data received during dissertation generation."}) + "\n"
-                                # Potentially return here too
-                    else:
-                        logging.warning("Tableau content is empty, skipping dissertation generation.")
-                        yield json.dumps({"type": "error", "error": "Tableau d'analyse non généré, impossible de créer la dissertation."}) + "\n"
-
-                    logging.info("Dissertation generation stream finished.")
-
-                except Exception as e:
-                    logging.error(f"Error during streaming generation: {e}", exc_info=True)
-                    # Yield a JSON error message for the client
-                    yield json.dumps({"type": "error", "error": f"Une erreur interne est survenue: {e}"}) + "\n"
-                finally:
-                    # Nettoyer le fichier temporaire in the finally block of generate
-                    # Ensure the image object is closed if necessary (PIL handles this reasonably well)
-                    # image.close() # Usually not needed with 'with open' or tempfile context
-                    pass # temp_file is closed by with statement, path needed for unlink
-
-            # Return the streaming response
-            # Make sure temp_file path is accessible for cleanup *after* streaming finishes
-            response = Response(generate(), content_type='text/event-stream')
-
-            # Use response.call_on_close for reliable cleanup after stream finishes/closes
-            if temp_file and os.path.exists(temp_file.name):
-                 cleanup_path = temp_file.name
-                 response.call_on_close(lambda: os.unlink(cleanup_path) if os.path.exists(cleanup_path) else None)
-                 logging.info(f"Scheduled cleanup for temp file: {cleanup_path}")
-
-
-            return response
+                            logging.error(f"Received invalid JSON from dissertation stream: {chunk_json}")
+                            yield json.dumps({"type": "error", "error": "Invalid data received during dissertation generation."}) + "\n"
+                else:
+                    logging.warning("Tableau content is empty, skipping dissertation generation.")
+                    yield json.dumps({"type": "error", "error": "Tableau d'analyse non généré, impossible de créer la dissertation."}) + "\n"
+
+                logging.info("Dissertation generation stream finished.")
+
+            except Exception as e:
+                logging.error(f"Error during streaming generation: {e}", exc_info=True)
+                yield json.dumps({"type": "error", "error": f"Une erreur interne est survenue: {e}"}) + "\n"
+            finally:
+                # Cleanup uploaded files from Gemini (optional, they expire automatically)
+                pass
+
+        response = Response(generate(), content_type='text/event-stream')
+
+        # Cleanup temp files after streaming
+        def cleanup_temp_files():
+            for temp_file in temp_files:
+                try:
+                    if os.path.exists(temp_file.name):
+                        os.unlink(temp_file.name)
+                        logging.info(f"Cleaned up temp file: {temp_file.name}")
+                except OSError as e:
+                    logging.error(f"Error cleaning up temp file {temp_file.name}: {e}")
+
+        response.call_on_close(cleanup_temp_files)
+
+        return response
 
     except Exception as e:
-        # Catch errors during file handling or initial PIL processing
-        logging.error(f"Error processing upload before streaming: {e}", exc_info=True)
-        # Ensure cleanup if temp_file was created before the error
-        if temp_file and os.path.exists(temp_file.name):
-             try:
-                 os.unlink(temp_file.name)
-                 logging.info(f"Cleaned up temp file due to pre-stream error: {temp_file.name}")
-             except OSError as unlink_error:
-                 logging.error(f"Error unlinking temp file during error handling: {unlink_error}")
-        return jsonify({'error': f'Error processing file: {e}'}), 500
-    # Note: The 'finally' block for the 'with tempfile' is implicitly handled
-    # but explicit cleanup using response.call_on_close is better for streaming
+        logging.error(f"Error processing uploads before streaming: {e}", exc_info=True)
+        # Cleanup any temp files created
+        for temp_file in temp_files:
+            try:
+                if os.path.exists(temp_file.name):
+                    os.unlink(temp_file.name)
+            except OSError:
+                pass
+        return jsonify({'error': f'Error processing files: {e}'}), 500
 
 
 # --- End Flask Routes ---

opencode.json

@@ -0,0 +1,22 @@
+{
+  "$schema": "https://opencode.ai/config.json",
+  "mcp": {
+    "context7": {
+      "type": "remote",
+      "url": "https://mcp.context7.com/mcp",
+      "headers": {
+        "CONTEXT7_API_KEY": "ctx7sk-7b0179e4-f420-4459-8eb8-da652aca9cb8"
+      },
+      "enabled": true
+    },
+    "sequential-thinking": {
+      "type": "local",
+      "command": [
+        "npx",
+        "-y",
+        "@modelcontextprotocol/server-sequential-thinking"
+      ],
+      "enabled": true
+    }
+  }
+}

templates/index.html

@@ -134,12 +134,24 @@
       overflow: hidden;
       position: relative;
     }
+    .preview-images-container {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 1rem;
+      justify-content: center;
+    }
+    .preview-image-container {
+      flex: 0 0 auto;
+      max-width: 200px;
+    }
     .preview-image {
       max-width: 100%;
       max-height: 15rem; /* Limite hauteur aperçu */
       height: auto;
       margin: 0 auto;
       display: block;
+      border-radius: var(--radius-md);
+      box-shadow: var(--shadow-sm);
     }
     .btn {
       display: block;
@@ -351,13 +363,13 @@
   <main>
     <div class="card">
       <form id="uploadForm">
-         <div class="input-group">
-          <label for="imageInput" class="label">Votre document</label>
+        <div class="input-group">
+          <label for="imageInput" class="label">Vos documents</label>
           <div id="uploadArea" class="upload-area">
             <svg class="upload-icon" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke="currentColor"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M7 16a4 4 0 01-.88-7.903A5 5 0 1115.9 6L16 6a5 5 0 011 9.9M15 13l-3-3m0 0l-3 3m3-3v12" /></svg>
             <p>Touchez pour télécharger</p>
-            <p class="upload-instructions">PNG, JPG jusqu'à 10MB</p>
-             <input id="imageInput" type="file" class="file-input" accept="image/*">
+            <p class="upload-instructions">PNG, JPG jusqu'à 10MB chacun (plusieurs fichiers possibles)</p>
+              <input id="imageInput" type="file" class="file-input" accept="image/*" multiple>
           </div>
         </div>
         <div class="input-group">
@@ -365,8 +377,8 @@
           <input id="consignesInput" type="text" class="input" placeholder="Entrez vos consignes ici">
         </div>
         <div id="previewContainer" class="preview-container" style="display: none;">
-          <p class="preview-title">Aperçu du document</p>
-          <div class="preview-image-container"><img id="previewImage" class="preview-image" src="#" alt="Aperçu"></div>
+          <p class="preview-title">Aperçu des documents</p>
+          <div id="previewImagesContainer" class="preview-images-container"></div>
         </div>
         <div class="button-container">
             <button type="submit" class="btn btn-primary">Lancer l'analyse standard</button>
@@ -496,22 +508,51 @@
     });
 
 
-     imageInput.addEventListener('change', function() { const file = this.files[0]; if (file) { const reader = new FileReader(); reader.onload = function(e) { previewImage.src = e.target.result; previewContainer.style.display = 'block'; previewContainer.classList.add('slide-up'); uploadArea.classList.add('active'); }; reader.readAsDataURL(file); } else { previewContainer.style.display = 'none'; uploadArea.classList.remove('active'); } updateButtonStates(); });
+      imageInput.addEventListener('change', function() {
+        const files = this.files;
+        const previewImagesContainer = document.getElementById('previewImagesContainer');
+        previewImagesContainer.innerHTML = ''; // Clear previous previews
+
+        if (files.length > 0) {
+          for (let i = 0; i < files.length; i++) {
+            const file = files[i];
+            const reader = new FileReader();
+            reader.onload = function(e) {
+              const container = document.createElement('div');
+              container.className = 'preview-image-container';
+              const img = document.createElement('img');
+              img.className = 'preview-image';
+              img.src = e.target.result;
+              img.alt = `Aperçu ${i + 1}`;
+              container.appendChild(img);
+              previewImagesContainer.appendChild(container);
+            };
+            reader.readAsDataURL(file);
+          }
+          previewContainer.style.display = 'block';
+          previewContainer.classList.add('slide-up');
+          uploadArea.classList.add('active');
+        } else {
+          previewContainer.style.display = 'none';
+          uploadArea.classList.remove('active');
+        }
+        updateButtonStates();
+      });
     dissertationTab.addEventListener('click', () => switchTab('dissertation'));
     tableauTab.addEventListener('click', () => switchTab('tableau'));
     function switchTab(tabName) { const isDissertation = tabName === 'dissertation'; dissertationTab.classList.toggle('active', isDissertation); tableauTab.classList.toggle('active', !isDissertation); dissertationContentWrapper.style.display = isDissertation ? 'block' : 'none'; tableauContentWrapper.style.display = !isDissertation ? 'block' : 'none'; }
 
-     // --- Button State Management ---
-     function updateButtonStates() {
-         const hasImage = imageInput.files.length > 0;
-         submitBtn.disabled = !hasImage;
+      // --- Button State Management ---
+      function updateButtonStates() {
+          const hasImages = imageInput.files.length > 0;
+          submitBtn.disabled = !hasImages;
 
-         const lastUsedDate = localStorage.getItem(DEEPTHINK_STORAGE_KEY);
-         const todayDate = new Date().toDateString();
-         const limitReached = lastUsedDate === todayDate;
-         deepThinkBtn.disabled = !hasImage || limitReached;
-         deepThinkBtn.textContent = limitReached ? "DeepThink (Utilisé)" : "DeepThink (1/jour)";
-     }
+          const lastUsedDate = localStorage.getItem(DEEPTHINK_STORAGE_KEY);
+          const todayDate = new Date().toDateString();
+          const limitReached = lastUsedDate === todayDate;
+          deepThinkBtn.disabled = !hasImages || limitReached;
+          deepThinkBtn.textContent = limitReached ? "DeepThink (Utilisé)" : "DeepThink (1/jour)";
+      }
 
      function recordDeepThinkUsage() {
          const todayDate = new Date().toDateString();
@@ -611,36 +652,42 @@
          .catch(error => { console.error('Erreur Fetch:', error); loadingIndicator.style.display = 'none'; alert("Une erreur de connexion s'est produite. Veuillez vérifier votre connexion internet et réessayer."); downloadPdfBtn.disabled = true; streamComplete = true; });
     }
 
-     // --- Form Submission Handlers ---
-     uploadForm.addEventListener('submit', function(e) {
-         e.preventDefault(); // Prevent default form submission
-
-         const formData = new FormData();
-         formData.append('image', imageInput.files[0]);
-         formData.append('consignes', consignesInput.value);
-         // NO use_deepthink flag here for standard analysis
-
-         startAnalysis(formData); // Call the reusable analysis function
+      // --- Form Submission Handlers ---
+      uploadForm.addEventListener('submit', function(e) {
+          e.preventDefault(); // Prevent default form submission
+
+          const formData = new FormData();
+          const files = imageInput.files;
+          for (let i = 0; i < files.length; i++) {
+            formData.append('images', files[i]);
+          }
+          formData.append('consignes', consignesInput.value);
+          // NO use_deepthink flag here for standard analysis
+
+          startAnalysis(formData); // Call the reusable analysis function
+       });
+
+      deepThinkBtn.addEventListener('click', function() {
+          // Since disabled when no image or limit reached, but double check limit
+          const lastUsedDate = localStorage.getItem(DEEPTHINK_STORAGE_KEY);
+          const todayDate = new Date().toDateString();
+          if(lastUsedDate === todayDate) {
+               alert("DeepThink a déjà été utilisé aujourd'hui.");
+               updateButtonStates(); // Update button state if needed
+               return;
+          }
+
+          const formData = new FormData();
+          const files = imageInput.files;
+          for (let i = 0; i < files.length; i++) {
+            formData.append('images', files[i]);
+          }
+          formData.append('consignes', consignesInput.value);
+          formData.append('use_deepthink', 'true'); // ADD THE FLAG FOR DEEPTHINK
+
+          startAnalysis(formData); // Call the reusable analysis function
       });
 
-     deepThinkBtn.addEventListener('click', function() {
-         // Since disabled when no image or limit reached, but double check limit
-         const lastUsedDate = localStorage.getItem(DEEPTHINK_STORAGE_KEY);
-         const todayDate = new Date().toDateString();
-         if(lastUsedDate === todayDate) {
-              alert("DeepThink a déjà été utilisé aujourd'hui.");
-              updateButtonStates(); // Update button state if needed
-              return;
-         }
-
-         const formData = new FormData();
-         formData.append('image', imageInput.files[0]);
-         formData.append('consignes', consignesInput.value);
-         formData.append('use_deepthink', 'true'); // ADD THE FLAG FOR DEEPTHINK
-
-         startAnalysis(formData); // Call the reusable analysis function
-     });
-
 
     // --- PDF Download Logic (Unchanged logic, should still work) ---
     downloadPdfBtn.addEventListener('click', () => {